Overview

Dataset statistics

Number of variables60
Number of observations346
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory162.3 KiB
Average record size in memory480.4 B

Variable types

Text17
Numeric23
Categorical20

Alerts

party_total_hpratio has constant value ""Constant
party_level6_spellslots is highly imbalanced (77.2%)Imbalance
party_level7_spellslots is highly imbalanced (82.1%)Imbalance
party_level8_spellslots is highly imbalanced (75.6%)Imbalance
party_level9_spellslots is highly imbalanced (76.9%)Imbalance
party_total_postcombat_hp is highly imbalanced (97.1%)Imbalance
Blood Hunter is highly imbalanced (68.1%)Imbalance
Bard is highly imbalanced (68.1%)Imbalance
Druid is highly imbalanced (67.0%)Imbalance
Sorcerer is highly imbalanced (52.7%)Imbalance
combat_id has unique valuesUnique
start_time has unique valuesUnique
monsters_info has unique valuesUnique
monster_total_level has 8 (2.3%) zerosZeros
party_level1_spellslots has 135 (39.0%) zerosZeros
party_level2_spellslots has 191 (55.2%) zerosZeros
party_level3_spellslots has 248 (71.7%) zerosZeros
party_level4_spellslots has 297 (85.8%) zerosZeros
party_level5_spellslots has 312 (90.2%) zerosZeros
weighted_monster_level has 8 (2.3%) zerosZeros
weighted_spell_slots has 113 (32.7%) zerosZeros

Reproduction

Analysis started2024-04-07 14:25:10.805744
Analysis finished2024-04-07 14:25:52.748987
Duration41.94 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

combat_id
Text

UNIQUE 

Distinct346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:52.840861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length47
Median length47
Mean length47
Min length47

Characters and Unicode

Total characters16262
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)100.0%

Sample

1st row1667093704-73130db4-c26b-4778-a242-0b98c46adabc
2nd row1658885575-ee4d97ac-453a-4de1-ac3d-a10dc227bb92
3rd row1664367987-7e065799-4cb1-4646-94a9-ad201547e443
4th row1664333782-19e43046-cc43-4927-8570-152502d1275d
5th row1665884477-91457c2f-4cde-4066-b9c4-77d8982d862e
ValueCountFrequency (%)
1667093704-73130db4-c26b-4778-a242-0b98c46adabc 1
 
0.3%
1662304134-6c3dd49b-7db4-4d9c-b7ee-c1077a59c7fc 1
 
0.3%
1665884477-91457c2f-4cde-4066-b9c4-77d8982d862e 1
 
0.3%
1666298872-b16d909c-53c6-4bfd-bcc0-f045390a2681 1
 
0.3%
1658768918-afcaaf67-a36f-4a62-acfb-23d9550c5316 1
 
0.3%
1658195387-67ab61f9-0167-4831-8b97-b4b9b1cecdd9 1
 
0.3%
1661531195-75b17922-a46a-4f25-8443-774d05ae2aed 1
 
0.3%
1658988238-60a0c6dd-9a56-4c8b-9bee-79d4839b7cf4 1
 
0.3%
1656724479-fb93353a-2a01-465f-8ca9-c43d78cc3c70 1
 
0.3%
1664169196-72b52408-3cb3-4cf3-a4f9-03e86f34480a 1
 
0.3%
Other values (336) 336
97.1%
2024-04-07T10:25:53.063627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1730
 
10.6%
6 1428
 
8.8%
1 1205
 
7.4%
4 1204
 
7.4%
5 1021
 
6.3%
8 994
 
6.1%
9 971
 
6.0%
2 922
 
5.7%
0 909
 
5.6%
7 891
 
5.5%
Other values (7) 4987
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1730
 
10.6%
6 1428
 
8.8%
1 1205
 
7.4%
4 1204
 
7.4%
5 1021
 
6.3%
8 994
 
6.1%
9 971
 
6.0%
2 922
 
5.7%
0 909
 
5.6%
7 891
 
5.5%
Other values (7) 4987
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1730
 
10.6%
6 1428
 
8.8%
1 1205
 
7.4%
4 1204
 
7.4%
5 1021
 
6.3%
8 994
 
6.1%
9 971
 
6.0%
2 922
 
5.7%
0 909
 
5.6%
7 891
 
5.5%
Other values (7) 4987
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1730
 
10.6%
6 1428
 
8.8%
1 1205
 
7.4%
4 1204
 
7.4%
5 1021
 
6.3%
8 994
 
6.1%
9 971
 
6.0%
2 922
 
5.7%
0 909
 
5.6%
7 891
 
5.5%
Other values (7) 4987
30.7%

start_time
Real number (ℝ)

UNIQUE 

Distinct346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6622777 × 109
Minimum1.6538699 × 109
Maximum1.6696697 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:53.184415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.6538699 × 109
5-th percentile1.6558454 × 109
Q11.6589112 × 109
median1.662574 × 109
Q31.6657917 × 109
95-th percentile1.6685685 × 109
Maximum1.6696697 × 109
Range15799835
Interquartile range (IQR)6880487.8

Descriptive statistics

Standard deviation4052264.3
Coefficient of variation (CV)0.0024377781
Kurtosis-1.0232526
Mean1.6622777 × 109
Median Absolute Deviation (MAD)3438292.6
Skewness-0.083951131
Sum5.751481 × 1011
Variance1.6420846 × 1013
MonotonicityNot monotonic
2024-04-07T10:25:53.371669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1667093704 1
 
0.3%
1663916515 1
 
0.3%
1664368517 1
 
0.3%
1654983573 1
 
0.3%
1666827943 1
 
0.3%
1658355727 1
 
0.3%
1659659214 1
 
0.3%
1660166575 1
 
0.3%
1661381702 1
 
0.3%
1664552014 1
 
0.3%
Other values (336) 336
97.1%
ValueCountFrequency (%)
1653869872 1
0.3%
1654013159 1
0.3%
1654464702 1
0.3%
1654743575 1
0.3%
1654776671 1
0.3%
1654827603 1
0.3%
1654956165 1
0.3%
1654983573 1
0.3%
1655137685 1
0.3%
1655185570 1
0.3%
ValueCountFrequency (%)
1669669707 1
0.3%
1669665510 1
0.3%
1669576455 1
0.3%
1669565971 1
0.3%
1669530427 1
0.3%
1669470296 1
0.3%
1669436842 1
0.3%
1669330263 1
0.3%
1669298884 1
0.3%
1669143606 1
0.3%
Distinct213
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:53.492000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length176
Median length22
Mean length28.803468
Min length22

Characters and Unicode

Total characters9966
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)47.7%

Sample

1st row['177558997848408361']
2nd row['127965999109502658']
3rd row['326793710699339197', '264022841131784635', '239987723820313556']
4th row['288900826835560174']
5th row['163631059791733886']
ValueCountFrequency (%)
163631059791733886 19
 
4.2%
208864991128741524 14
 
3.1%
163432491052587407 12
 
2.6%
327601077009165698 10
 
2.2%
321444462285149813 7
 
1.5%
511311320649900416 7
 
1.5%
120016505467761711 7
 
1.5%
310319411472492601 7
 
1.5%
640172978900337268 6
 
1.3%
176588704091156599 6
 
1.3%
Other values (252) 358
79.0%
2024-04-07T10:25:53.723333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 987
9.9%
' 906
9.1%
3 894
9.0%
2 861
8.6%
6 829
8.3%
0 788
7.9%
9 772
7.7%
4 765
7.7%
7 761
7.6%
5 758
7.6%
Other values (5) 1645
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 987
9.9%
' 906
9.1%
3 894
9.0%
2 861
8.6%
6 829
8.3%
0 788
7.9%
9 772
7.7%
4 765
7.7%
7 761
7.6%
5 758
7.6%
Other values (5) 1645
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 987
9.9%
' 906
9.1%
3 894
9.0%
2 861
8.6%
6 829
8.3%
0 788
7.9%
9 772
7.7%
4 765
7.7%
7 761
7.6%
5 758
7.6%
Other values (5) 1645
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 987
9.9%
' 906
9.1%
3 894
9.0%
2 861
8.6%
6 829
8.3%
0 788
7.9%
9 772
7.7%
4 765
7.7%
7 761
7.6%
5 758
7.6%
Other values (5) 1645
16.5%
Distinct303
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:53.851529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5004
Median length1801
Mean length493.28324
Min length346

Characters and Unicode

Total characters170676
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)79.8%

Sample

1st row[{'hp_ratio': (0, 23), 'class': [('Warlock', 4)], 'slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 2, 'strength': 20, 'dexterity': 9, 'constitution': 10, 'intelligence': 12, 'wisdom': 14, 'charisma': 19}}]
2nd row[{'hp_ratio': (0, 75), 'class': [('Bard', 8)], 'slots': {'1': 4, '2': 1, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 7, 'constitution': 18, 'intelligence': 15, 'wisdom': 15, 'charisma': 17}}]
3rd row[{'hp_ratio': None, 'class': [('Rogue', 9)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 4, 'strength': 19, 'dexterity': 20, 'constitution': 16, 'intelligence': 14, 'wisdom': 10, 'charisma': 10}}, {'hp_ratio': None, 'class': [('Blood Hunter', 9)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 20, 'stats': {'prof_bonus': 4, 'strength': 21, 'dexterity': 14, 'constitution': 20, 'intelligence': 11, 'wisdom': 16, 'charisma': 8}}, {'hp_ratio': (0, 81), 'class': [('Warlock', 13)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 5, 'strength': 15, 'dexterity': 18, 'constitution': 12, 'intelligence': 17, 'wisdom': 11, 'charisma': 13}}]
4th row[{'hp_ratio': (0, 114), 'class': [('Fighter', 11)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 4, 'strength': 20, 'dexterity': 10, 'constitution': 19, 'intelligence': 12, 'wisdom': 12, 'charisma': 12}}]
5th row[{'hp_ratio': (0, 48), 'class': [('Cleric', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 3, 'strength': 16, 'dexterity': 14, 'constitution': 16, 'intelligence': 10, 'wisdom': 16, 'charisma': 12}}]
ValueCountFrequency (%)
0 7449
25.9%
3 1706
 
5.9%
2 1598
 
5.5%
4 1380
 
4.8%
1 1197
 
4.2%
8 1106
 
3.8%
9 1053
 
3.7%
6 1042
 
3.6%
5 1034
 
3.6%
7 985
 
3.4%
Other values (157) 10254
35.6%
2024-04-07T10:25:54.086483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 30234
17.7%
28458
16.7%
: 14508
 
8.5%
, 13930
 
8.2%
0 8068
 
4.7%
t 6201
 
3.6%
s 6084
 
3.6%
1 4314
 
2.5%
i 4082
 
2.4%
o 4021
 
2.4%
Other values (45) 50776
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 170676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 30234
17.7%
28458
16.7%
: 14508
 
8.5%
, 13930
 
8.2%
0 8068
 
4.7%
t 6201
 
3.6%
s 6084
 
3.6%
1 4314
 
2.5%
i 4082
 
2.4%
o 4021
 
2.4%
Other values (45) 50776
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 170676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 30234
17.7%
28458
16.7%
: 14508
 
8.5%
, 13930
 
8.2%
0 8068
 
4.7%
t 6201
 
3.6%
s 6084
 
3.6%
1 4314
 
2.5%
i 4082
 
2.4%
o 4021
 
2.4%
Other values (45) 50776
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 170676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 30234
17.7%
28458
16.7%
: 14508
 
8.5%
, 13930
 
8.2%
0 8068
 
4.7%
t 6201
 
3.6%
s 6084
 
3.6%
1 4314
 
2.5%
i 4082
 
2.4%
o 4021
 
2.4%
Other values (45) 50776
29.7%

monsters_info
Text

UNIQUE 

Distinct346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:54.255955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length13372
Median length1389.5
Mean length485.3237
Min length117

Characters and Unicode

Total characters167922
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)100.0%

Sample

1st row[{'monster_id': '4c7d90bb-9255-44a9-aa4e-f6ffa1fa9000', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'b5491d16-ca83-4996-82d1-41d1ec1b7db6', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '6ede1067-ed54-42bb-9ce2-14068477164c', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '15406585-e135-4bde-b13a-95e9039052c4', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'cba094e4-cbda-4cb3-b96d-6783a378cba9', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '535a9ef0-3c3c-4719-824c-761ee6ba0675', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': '18ae3599-3f5f-434c-abf4-08985400956c', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5b665adc-16fb-4a81-93da-3476c692d470', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5369b5d1-45e9-4242-8a9b-1d4280822a1e', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5a49cbdf-9890-4826-bb02-98a9f60cf367', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'f752f71e-0840-4f4c-af8e-73cd169acf8a', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'ff186863-f2ab-40f0-b194-8e2f4965b444', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '62baee43-b320-414a-b2fb-1010a1635bf0', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '0162fa37-9f5a-4be5-b820-437ef078ae4e', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'd1084b11-b70b-4b0b-826d-ef22e6237863', 'monster_code': 'SoR2', 'monster_name': 'Swarm of Rats', 'level': 0.25}]
2nd row[{'monster_id': 'c20a1c98-e82c-4906-893d-0a9ebb2ba3ef', 'monster_code': 'HO1', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': 'e074b563-a6be-4b87-8532-ae0b2782bbb9', 'monster_code': 'HO2', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '6d0a08d1-c8f0-445d-a510-a36e343710d8', 'monster_code': 'HO3', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '1b3b4139-47a2-45eb-a3cf-a3fd0f2f9bfa', 'monster_code': 'HO4', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': 'cdcfd1ca-1dcc-460d-a2e3-374badb50c35', 'monster_code': 'HO5', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '5561f699-49ad-4b38-83f4-c7cd88548c74', 'monster_code': 'HO6', 'monster_name': 'Half-Ogre', 'level': 1.0}]
3rd row[{'monster_id': '89bca030-87a3-47a3-9621-032fe23ba0a8', 'monster_code': 'DR2', 'monster_name': 'Drow', 'level': 0.25}, {'monster_id': 'e3ab898a-4f96-4cd4-9f8a-fa66394a7b14', 'monster_code': 'DR1', 'monster_name': 'Drow', 'level': 0.25}, {'monster_id': 'f028f6c8-3811-4a6e-bce0-3178cfcb9e9c', 'monster_code': 'KN1', 'monster_name': 'Knight', 'level': 3.0}, {'monster_id': '08ccd782-f99c-449b-9bbc-7441d5738c7a', 'monster_code': 'KN1', 'monster_name': 'Knight', 'level': 3.0}]
4th row[{'monster_id': '51ceaa89-0155-46e4-9406-58a4c8d0a4ad', 'monster_code': 'FDT1', 'monster_name': 'Forsaken Dire Troll', 'level': 15.0}]
5th row[{'monster_id': 'd9d0a661-232b-4db3-83d3-960b6c78f1ba', 'monster_code': 'HC1', 'monster_name': 'Hobgoblin Captain', 'level': 3.0}, {'monster_id': 'aebce6db-abbe-4c08-94bf-e8cf24b529b6', 'monster_code': 'HO1', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '24662a24-d25f-4044-b37b-e901f7e242b0', 'monster_code': 'HO2', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'f85a3e03-ffa8-44e0-b4f6-c28e1442ec6e', 'monster_code': 'HO5', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'ce37caa6-688f-4a32-b421-eaf3a42f37bc', 'monster_code': 'HO3', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'aed813cc-0907-487a-b51f-65b3b26fb096', 'monster_code': 'HO6', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '3be5a1e2-9f5e-4ee2-b318-90c0431f0b03', 'monster_code': 'HO7', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '57a6b71b-e49b-45ee-9b4a-38b9e9f07002', 'monster_code': 'HO8', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'e57987d8-2069-4f9c-8630-1927b99f9221', 'monster_code': 'HO4', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '22f1665b-687a-4d5c-89b3-ceccb4b8223a', 'monster_code': 'HO9', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '7fa63916-0a40-455a-b19b-5739d049e1ea', 'monster_code': 'GB1', 'monster_name': 'Giant Boar', 'level': 2.0}]
ValueCountFrequency (%)
level 1349
 
11.5%
monster_id 1347
 
11.5%
monster_code 1347
 
11.5%
monster_name 1347
 
11.5%
0.125 191
 
1.6%
0.25 154
 
1.3%
1.0 153
 
1.3%
0.5 152
 
1.3%
2.0 147
 
1.3%
giant 130
 
1.1%
Other values (2595) 5388
46.0%
2024-04-07T10:25:54.545791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 18845
 
11.2%
e 13102
 
7.8%
11359
 
6.8%
o 6417
 
3.8%
n 6385
 
3.8%
d 5665
 
3.4%
m 5642
 
3.4%
- 5460
 
3.3%
a 5411
 
3.2%
: 5388
 
3.2%
Other values (72) 84248
50.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 167922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 18845
 
11.2%
e 13102
 
7.8%
11359
 
6.8%
o 6417
 
3.8%
n 6385
 
3.8%
d 5665
 
3.4%
m 5642
 
3.4%
- 5460
 
3.3%
a 5411
 
3.2%
: 5388
 
3.2%
Other values (72) 84248
50.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 167922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 18845
 
11.2%
e 13102
 
7.8%
11359
 
6.8%
o 6417
 
3.8%
n 6385
 
3.8%
d 5665
 
3.4%
m 5642
 
3.4%
- 5460
 
3.3%
a 5411
 
3.2%
: 5388
 
3.2%
Other values (72) 84248
50.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 167922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 18845
 
11.2%
e 13102
 
7.8%
11359
 
6.8%
o 6417
 
3.8%
n 6385
 
3.8%
d 5665
 
3.4%
m 5642
 
3.4%
- 5460
 
3.3%
a 5411
 
3.2%
: 5388
 
3.2%
Other values (72) 84248
50.2%

party_size
Real number (ℝ)

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3092486
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:54.645829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83737063
Coefficient of variation (CV)0.6395811
Kurtosis18.016958
Mean1.3092486
Median Absolute Deviation (MAD)0
Skewness3.7455024
Sum453
Variance0.70118958
MonotonicityNot monotonic
2024-04-07T10:25:54.721543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 287
82.9%
2 31
 
9.0%
3 17
 
4.9%
4 6
 
1.7%
5 3
 
0.9%
6 1
 
0.3%
8 1
 
0.3%
ValueCountFrequency (%)
1 287
82.9%
2 31
 
9.0%
3 17
 
4.9%
4 6
 
1.7%
5 3
 
0.9%
6 1
 
0.3%
8 1
 
0.3%
ValueCountFrequency (%)
8 1
 
0.3%
6 1
 
0.3%
5 3
 
0.9%
4 6
 
1.7%
3 17
 
4.9%
2 31
 
9.0%
1 287
82.9%
Distinct86
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:54.779139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length74
Median length72
Mean length72.031792
Min length72

Characters and Unicode

Total characters24923
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)17.1%

Sample

1st row{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
2nd row{'1': 4, '2': 1, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
3rd row{'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}
4th row{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
5th row{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
ValueCountFrequency (%)
0 2507
40.3%
3 550
 
8.8%
2 511
 
8.2%
4 446
 
7.2%
1 430
 
6.9%
6 360
 
5.8%
5 357
 
5.7%
8 355
 
5.7%
7 354
 
5.7%
9 347
 
5.6%
Other values (7) 11
 
0.2%
2024-04-07T10:25:54.944883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2510
10.1%
3 550
 
2.2%
2 514
 
2.1%
4 447
 
1.8%
1 441
 
1.8%
6 361
 
1.4%
Other values (6) 2108
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2510
10.1%
3 550
 
2.2%
2 514
 
2.1%
4 447
 
1.8%
1 441
 
1.8%
6 361
 
1.4%
Other values (6) 2108
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2510
10.1%
3 550
 
2.2%
2 514
 
2.1%
4 447
 
1.8%
1 441
 
1.8%
6 361
 
1.4%
Other values (6) 2108
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2510
10.1%
3 550
 
2.2%
2 514
 
2.1%
4 447
 
1.8%
1 441
 
1.8%
6 361
 
1.4%
Other values (6) 2108
 
8.5%
Distinct64
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:55.015501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length74
Median length72
Mean length72.037572
Min length72

Characters and Unicode

Total characters24925
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)11.6%

Sample

1st row{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
2nd row{'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
3rd row{'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}
4th row{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
5th row{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
ValueCountFrequency (%)
0 2469
39.6%
3 571
 
9.2%
2 497
 
8.0%
4 478
 
7.7%
1 420
 
6.7%
6 363
 
5.8%
8 358
 
5.7%
5 357
 
5.7%
7 353
 
5.7%
9 349
 
5.6%
Other values (7) 13
 
0.2%
2024-04-07T10:25:55.179409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2472
 
9.9%
3 571
 
2.3%
2 500
 
2.0%
4 479
 
1.9%
1 435
 
1.7%
6 364
 
1.5%
Other values (6) 2112
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2472
 
9.9%
3 571
 
2.3%
2 500
 
2.0%
4 479
 
1.9%
1 435
 
1.7%
6 364
 
1.5%
Other values (6) 2112
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2472
 
9.9%
3 571
 
2.3%
2 500
 
2.0%
4 479
 
1.9%
1 435
 
1.7%
6 364
 
1.5%
Other values (6) 2112
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 6228
25.0%
5882
23.6%
: 3114
12.5%
, 2768
11.1%
0 2472
 
9.9%
3 571
 
2.3%
2 500
 
2.0%
4 479
 
1.9%
1 435
 
1.7%
6 364
 
1.5%
Other values (6) 2112
 
8.5%
Distinct225
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:55.273612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length208
Median length94
Mean length27.635838
Min length2

Characters and Unicode

Total characters9562
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)50.9%

Sample

1st row[('Warlock', 4)]
2nd row[('Bard', 8)]
3rd row[('Rogue', 9), ('Blood Hunter', 9), ('Warlock', 13)]
4th row[('Fighter', 11)]
5th row[('Cleric', 5)]
ValueCountFrequency (%)
fighter 97
 
7.8%
3 92
 
7.4%
2 90
 
7.3%
1 88
 
7.1%
4 82
 
6.6%
cleric 73
 
5.9%
5 66
 
5.3%
warlock 55
 
4.4%
barbarian 53
 
4.3%
rogue 53
 
4.3%
Other values (25) 490
39.5%
2024-04-07T10:25:55.477521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 1218
 
12.7%
893
 
9.3%
, 873
 
9.1%
) 609
 
6.4%
( 609
 
6.4%
r 595
 
6.2%
a 412
 
4.3%
e 358
 
3.7%
] 346
 
3.6%
[ 346
 
3.6%
Other values (33) 3303
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 1218
 
12.7%
893
 
9.3%
, 873
 
9.1%
) 609
 
6.4%
( 609
 
6.4%
r 595
 
6.2%
a 412
 
4.3%
e 358
 
3.7%
] 346
 
3.6%
[ 346
 
3.6%
Other values (33) 3303
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 1218
 
12.7%
893
 
9.3%
, 873
 
9.1%
) 609
 
6.4%
( 609
 
6.4%
r 595
 
6.2%
a 412
 
4.3%
e 358
 
3.7%
] 346
 
3.6%
[ 346
 
3.6%
Other values (33) 3303
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 1218
 
12.7%
893
 
9.3%
, 873
 
9.1%
) 609
 
6.4%
( 609
 
6.4%
r 595
 
6.2%
a 412
 
4.3%
e 358
 
3.7%
] 346
 
3.6%
[ 346
 
3.6%
Other values (33) 3303
34.5%
Distinct111
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:55.563734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length138
Median length64
Mean length18.601156
Min length2

Characters and Unicode

Total characters6436
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)21.4%

Sample

1st row['Warlock']
2nd row['Bard']
3rd row['Rogue', 'Blood Hunter', 'Warlock']
4th row['Fighter']
5th row['Cleric']
ValueCountFrequency (%)
fighter 97
15.4%
cleric 73
11.6%
warlock 55
8.7%
rogue 53
8.4%
barbarian 53
8.4%
wizard 51
8.1%
monk 45
7.1%
paladin 43
6.8%
ranger 41
6.5%
sorcerer 37
 
5.9%
Other values (5) 82
13.0%
2024-04-07T10:25:55.769878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 1218
18.9%
r 595
 
9.2%
a 412
 
6.4%
e 358
 
5.6%
[ 346
 
5.4%
] 346
 
5.4%
i 338
 
5.3%
284
 
4.4%
, 264
 
4.1%
o 230
 
3.6%
Other values (21) 2045
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 1218
18.9%
r 595
 
9.2%
a 412
 
6.4%
e 358
 
5.6%
[ 346
 
5.4%
] 346
 
5.4%
i 338
 
5.3%
284
 
4.4%
, 264
 
4.1%
o 230
 
3.6%
Other values (21) 2045
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 1218
18.9%
r 595
 
9.2%
a 412
 
6.4%
e 358
 
5.6%
[ 346
 
5.4%
] 346
 
5.4%
i 338
 
5.3%
284
 
4.4%
, 264
 
4.1%
o 230
 
3.6%
Other values (21) 2045
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 1218
18.9%
r 595
 
9.2%
a 412
 
6.4%
e 358
 
5.6%
[ 346
 
5.4%
] 346
 
5.4%
i 338
 
5.3%
284
 
4.4%
, 264
 
4.1%
o 230
 
3.6%
Other values (21) 2045
31.8%
Distinct163
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:55.900567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length63
Median length9
Mean length11.16474
Min length8

Characters and Unicode

Total characters3863
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)28.0%

Sample

1st row[(0, 23)]
2nd row[(0, 75)]
3rd row[(0, 81)]
4th row[(0, 114)]
5th row[(0, 48)]
ValueCountFrequency (%)
0 420
49.9%
24 20
 
2.4%
44 17
 
2.0%
27 16
 
1.9%
31 16
 
1.9%
32 12
 
1.4%
43 10
 
1.2%
49 10
 
1.2%
35 9
 
1.1%
17 9
 
1.1%
Other values (122) 303
36.0%
2024-04-07T10:25:56.127896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 496
12.8%
496
12.8%
0 478
12.4%
( 421
10.9%
) 421
10.9%
[ 346
9.0%
] 346
9.0%
1 152
 
3.9%
2 146
 
3.8%
4 138
 
3.6%
Other values (6) 423
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 496
12.8%
496
12.8%
0 478
12.4%
( 421
10.9%
) 421
10.9%
[ 346
9.0%
] 346
9.0%
1 152
 
3.9%
2 146
 
3.8%
4 138
 
3.6%
Other values (6) 423
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 496
12.8%
496
12.8%
0 478
12.4%
( 421
10.9%
) 421
10.9%
[ 346
9.0%
] 346
9.0%
1 152
 
3.9%
2 146
 
3.8%
4 138
 
3.6%
Other values (6) 423
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 496
12.8%
496
12.8%
0 478
12.4%
( 421
10.9%
) 421
10.9%
[ 346
9.0%
] 346
9.0%
1 152
 
3.9%
2 146
 
3.8%
4 138
 
3.6%
Other values (6) 423
11.0%
Distinct75
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:56.221116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length56
Median length4
Mean length5.4104046
Min length4

Characters and Unicode

Total characters1872
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)15.0%

Sample

1st row[19]
2nd row[19]
3rd row[17, 20, 16]
4th row[19]
5th row[16]
ValueCountFrequency (%)
18 83
17.7%
16 66
14.1%
14 50
10.7%
17 49
10.5%
19 46
9.8%
15 44
9.4%
20 32
 
6.8%
13 19
 
4.1%
10 15
 
3.2%
21 13
 
2.8%
Other values (9) 51
10.9%
2024-04-07T10:25:56.382223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 411
22.0%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
2 100
 
5.3%
8 84
 
4.5%
6 68
 
3.6%
4 56
 
3.0%
7 49
 
2.6%
Other values (4) 168
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 411
22.0%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
2 100
 
5.3%
8 84
 
4.5%
6 68
 
3.6%
4 56
 
3.0%
7 49
 
2.6%
Other values (4) 168
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 411
22.0%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
2 100
 
5.3%
8 84
 
4.5%
6 68
 
3.6%
4 56
 
3.0%
7 49
 
2.6%
Other values (4) 168
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 411
22.0%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
2 100
 
5.3%
8 84
 
4.5%
6 68
 
3.6%
4 56
 
3.0%
7 49
 
2.6%
Other values (4) 168
9.0%
Distinct31
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
[2]
114 
[3]
77 
[4]
39 
[6]
33 
[5]
18 
Other values (26)
65 

Length

Max length42
Median length3
Mean length4.0578035
Min length3

Characters and Unicode

Total characters1404
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)3.8%

Sample

1st row[2]
2nd row[3]
3rd row[4, 4, 5]
4th row[4]
5th row[3]

Common Values

ValueCountFrequency (%)
[2] 114
32.9%
[3] 77
22.3%
[4] 39
 
11.3%
[6] 33
 
9.5%
[5] 18
 
5.2%
[2, 2] 12
 
3.5%
[2, 2, 2] 5
 
1.4%
[3, 3] 5
 
1.4%
[2, 3] 5
 
1.4%
[3, 2] 4
 
1.2%
Other values (21) 34
 
9.8%

Length

2024-04-07T10:25:56.488847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 210
44.9%
3 132
28.2%
4 52
 
11.1%
6 50
 
10.7%
5 23
 
4.9%
7 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
[ 346
24.6%
] 346
24.6%
2 210
15.0%
3 132
 
9.4%
, 122
 
8.7%
122
 
8.7%
4 52
 
3.7%
6 50
 
3.6%
5 23
 
1.6%
7 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 346
24.6%
] 346
24.6%
2 210
15.0%
3 132
 
9.4%
, 122
 
8.7%
122
 
8.7%
4 52
 
3.7%
6 50
 
3.6%
5 23
 
1.6%
7 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 346
24.6%
] 346
24.6%
2 210
15.0%
3 132
 
9.4%
, 122
 
8.7%
122
 
8.7%
4 52
 
3.7%
6 50
 
3.6%
5 23
 
1.6%
7 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 346
24.6%
] 346
24.6%
2 210
15.0%
3 132
 
9.4%
, 122
 
8.7%
122
 
8.7%
4 52
 
3.7%
6 50
 
3.6%
5 23
 
1.6%
7 1
 
0.1%
Distinct78
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:56.562854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length4
Mean length5.1560694
Min length3

Characters and Unicode

Total characters1784
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)17.6%

Sample

1st row[20]
2nd row[19]
3rd row[19, 21, 15]
4th row[20]
5th row[16]
ValueCountFrequency (%)
16 50
10.7%
18 49
10.5%
10 48
10.3%
15 47
10.0%
8 43
9.2%
12 36
7.7%
9 34
7.3%
13 32
6.8%
11 30
6.4%
20 30
6.4%
Other values (9) 69
14.7%
2024-04-07T10:25:56.734193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 379
21.2%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
8 92
 
5.2%
0 78
 
4.4%
2 73
 
4.1%
6 55
 
3.1%
9 50
 
2.8%
Other values (4) 121
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 379
21.2%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
8 92
 
5.2%
0 78
 
4.4%
2 73
 
4.1%
6 55
 
3.1%
9 50
 
2.8%
Other values (4) 121
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 379
21.2%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
8 92
 
5.2%
0 78
 
4.4%
2 73
 
4.1%
6 55
 
3.1%
9 50
 
2.8%
Other values (4) 121
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 379
21.2%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
8 92
 
5.2%
0 78
 
4.4%
2 73
 
4.1%
6 55
 
3.1%
9 50
 
2.8%
Other values (4) 121
 
6.8%
Distinct70
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:56.815843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length55
Median length4
Mean length5.3699422
Min length3

Characters and Unicode

Total characters1858
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)13.0%

Sample

1st row[9]
2nd row[7]
3rd row[20, 14, 18]
4th row[10]
5th row[14]
ValueCountFrequency (%)
14 86
18.4%
16 74
15.8%
20 65
13.9%
18 51
10.9%
12 30
 
6.4%
13 27
 
5.8%
17 26
 
5.6%
10 24
 
5.1%
11 22
 
4.7%
19 22
 
4.7%
Other values (6) 41
8.8%
2024-04-07T10:25:56.979315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 406
21.9%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 105
 
5.7%
0 89
 
4.8%
4 86
 
4.6%
6 79
 
4.3%
8 53
 
2.9%
Other values (4) 104
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 406
21.9%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 105
 
5.7%
0 89
 
4.8%
4 86
 
4.6%
6 79
 
4.3%
8 53
 
2.9%
Other values (4) 104
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 406
21.9%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 105
 
5.7%
0 89
 
4.8%
4 86
 
4.6%
6 79
 
4.3%
8 53
 
2.9%
Other values (4) 104
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 406
21.9%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 105
 
5.7%
0 89
 
4.8%
4 86
 
4.6%
6 79
 
4.3%
8 53
 
2.9%
Other values (4) 104
 
5.6%
Distinct68
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:57.058453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length56
Median length4
Mean length5.4046243
Min length4

Characters and Unicode

Total characters1870
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)15.3%

Sample

1st row[10]
2nd row[18]
3rd row[16, 20, 12]
4th row[19]
5th row[16]
ValueCountFrequency (%)
16 104
22.2%
14 94
20.1%
15 47
10.0%
18 45
9.6%
20 37
 
7.9%
12 36
 
7.7%
13 36
 
7.7%
17 28
 
6.0%
19 22
 
4.7%
10 12
 
2.6%
Other values (3) 7
 
1.5%
2024-04-07T10:25:57.217079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 434
23.2%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
6 104
 
5.6%
4 95
 
5.1%
2 73
 
3.9%
0 49
 
2.6%
5 47
 
2.5%
Other values (4) 132
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 434
23.2%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
6 104
 
5.6%
4 95
 
5.1%
2 73
 
3.9%
0 49
 
2.6%
5 47
 
2.5%
Other values (4) 132
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 434
23.2%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
6 104
 
5.6%
4 95
 
5.1%
2 73
 
3.9%
0 49
 
2.6%
5 47
 
2.5%
Other values (4) 132
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 434
23.2%
[ 346
18.5%
] 346
18.5%
, 122
 
6.5%
122
 
6.5%
6 104
 
5.6%
4 95
 
5.1%
2 73
 
3.9%
0 49
 
2.6%
5 47
 
2.5%
Other values (4) 132
 
7.1%
Distinct70
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:57.298755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length4
Mean length5.1589595
Min length3

Characters and Unicode

Total characters1785
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)14.7%

Sample

1st row[12]
2nd row[15]
3rd row[14, 11, 17]
4th row[12]
5th row[10]
ValueCountFrequency (%)
10 83
17.7%
12 74
15.8%
11 63
13.5%
8 51
10.9%
13 39
8.3%
14 34
7.3%
9 30
 
6.4%
16 22
 
4.7%
20 18
 
3.8%
18 16
 
3.4%
Other values (7) 38
8.1%
2024-04-07T10:25:57.461520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 425
23.8%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
0 101
 
5.7%
2 94
 
5.3%
8 67
 
3.8%
9 41
 
2.3%
3 39
 
2.2%
Other values (4) 82
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 425
23.8%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
0 101
 
5.7%
2 94
 
5.3%
8 67
 
3.8%
9 41
 
2.3%
3 39
 
2.2%
Other values (4) 82
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 425
23.8%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
0 101
 
5.7%
2 94
 
5.3%
8 67
 
3.8%
9 41
 
2.3%
3 39
 
2.2%
Other values (4) 82
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 425
23.8%
[ 346
19.4%
] 346
19.4%
, 122
 
6.8%
122
 
6.8%
0 101
 
5.7%
2 94
 
5.3%
8 67
 
3.8%
9 41
 
2.3%
3 39
 
2.2%
Other values (4) 82
 
4.6%
Distinct76
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:57.545276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length4
Mean length5.3699422
Min length3

Characters and Unicode

Total characters1858
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)17.6%

Sample

1st row[14]
2nd row[15]
3rd row[10, 16, 11]
4th row[12]
5th row[16]
ValueCountFrequency (%)
14 80
17.1%
16 75
16.0%
13 60
12.8%
12 50
10.7%
11 38
8.1%
10 36
7.7%
20 31
 
6.6%
18 30
 
6.4%
15 22
 
4.7%
17 20
 
4.3%
Other values (5) 26
 
5.6%
2024-04-07T10:25:57.705889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 459
24.7%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 85
 
4.6%
4 80
 
4.3%
6 75
 
4.0%
0 67
 
3.6%
3 60
 
3.2%
Other values (4) 96
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 459
24.7%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 85
 
4.6%
4 80
 
4.3%
6 75
 
4.0%
0 67
 
3.6%
3 60
 
3.2%
Other values (4) 96
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 459
24.7%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 85
 
4.6%
4 80
 
4.3%
6 75
 
4.0%
0 67
 
3.6%
3 60
 
3.2%
Other values (4) 96
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 459
24.7%
[ 346
18.6%
] 346
18.6%
, 122
 
6.6%
122
 
6.6%
2 85
 
4.6%
4 80
 
4.3%
6 75
 
4.0%
0 67
 
3.6%
3 60
 
3.2%
Other values (4) 96
 
5.2%
Distinct75
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:57.872430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length4
Mean length5.1965318
Min length3

Characters and Unicode

Total characters1798
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)16.5%

Sample

1st row[19]
2nd row[17]
3rd row[10, 8, 13]
4th row[12]
5th row[12]
ValueCountFrequency (%)
12 65
13.9%
10 51
10.9%
20 47
10.0%
11 44
9.4%
13 43
9.2%
8 41
8.8%
18 33
7.1%
14 32
6.8%
16 30
6.4%
15 27
5.8%
Other values (7) 55
11.8%
2024-04-07T10:25:58.038790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 390
21.7%
[ 346
19.2%
] 346
19.2%
, 122
 
6.8%
122
 
6.8%
2 114
 
6.3%
0 98
 
5.5%
8 74
 
4.1%
3 43
 
2.4%
9 35
 
1.9%
Other values (4) 108
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 390
21.7%
[ 346
19.2%
] 346
19.2%
, 122
 
6.8%
122
 
6.8%
2 114
 
6.3%
0 98
 
5.5%
8 74
 
4.1%
3 43
 
2.4%
9 35
 
1.9%
Other values (4) 108
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 390
21.7%
[ 346
19.2%
] 346
19.2%
, 122
 
6.8%
122
 
6.8%
2 114
 
6.3%
0 98
 
5.5%
8 74
 
4.1%
3 43
 
2.4%
9 35
 
1.9%
Other values (4) 108
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 390
21.7%
[ 346
19.2%
] 346
19.2%
, 122
 
6.8%
122
 
6.8%
2 114
 
6.3%
0 98
 
5.5%
8 74
 
4.1%
3 43
 
2.4%
9 35
 
1.9%
Other values (4) 108
 
6.0%
Distinct308
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:58.197221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length1368
Median length177
Mean length55.427746
Min length8

Characters and Unicode

Total characters19178
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique287 ?
Unique (%)82.9%

Sample

1st row['Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Swarm of Rats']
2nd row['Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre']
3rd row['Drow', 'Drow', 'Knight', 'Knight']
4th row['Forsaken Dire Troll']
5th row['Hobgoblin Captain', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Giant Boar']
ValueCountFrequency (%)
giant 127
 
5.8%
orc 81
 
3.7%
rat 61
 
2.8%
bandit 51
 
2.3%
goblin 43
 
2.0%
of 42
 
1.9%
awakened 39
 
1.8%
zombie 38
 
1.7%
marigold 32
 
1.5%
shrub 31
 
1.4%
Other values (503) 1649
75.2%
2024-04-07T10:25:58.480903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 2682
 
14.0%
1848
 
9.6%
a 1113
 
5.8%
e 1025
 
5.3%
, 1009
 
5.3%
r 1005
 
5.2%
i 990
 
5.2%
n 909
 
4.7%
o 890
 
4.6%
t 705
 
3.7%
Other values (59) 7002
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 2682
 
14.0%
1848
 
9.6%
a 1113
 
5.8%
e 1025
 
5.3%
, 1009
 
5.3%
r 1005
 
5.2%
i 990
 
5.2%
n 909
 
4.7%
o 890
 
4.6%
t 705
 
3.7%
Other values (59) 7002
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 2682
 
14.0%
1848
 
9.6%
a 1113
 
5.8%
e 1025
 
5.3%
, 1009
 
5.3%
r 1005
 
5.2%
i 990
 
5.2%
n 909
 
4.7%
o 890
 
4.6%
t 705
 
3.7%
Other values (59) 7002
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 2682
 
14.0%
1848
 
9.6%
a 1113
 
5.8%
e 1025
 
5.3%
, 1009
 
5.3%
r 1005
 
5.2%
i 990
 
5.2%
n 909
 
4.7%
o 890
 
4.6%
t 705
 
3.7%
Other values (59) 7002
36.5%

monster_number
Real number (ℝ)

Distinct22
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8930636
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:58.588789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum109
Range108
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.0326451
Coefficient of variation (CV)1.8064552
Kurtosis145.3545
Mean3.8930636
Median Absolute Deviation (MAD)1
Skewness10.196241
Sum1347
Variance49.458097
MonotonicityNot monotonic
2024-04-07T10:25:58.668640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 168
48.6%
2 39
 
11.3%
3 28
 
8.1%
5 21
 
6.1%
6 19
 
5.5%
4 16
 
4.6%
7 10
 
2.9%
13 7
 
2.0%
8 7
 
2.0%
11 5
 
1.4%
Other values (12) 26
 
7.5%
ValueCountFrequency (%)
1 168
48.6%
2 39
 
11.3%
3 28
 
8.1%
4 16
 
4.6%
5 21
 
6.1%
6 19
 
5.5%
7 10
 
2.9%
8 7
 
2.0%
9 5
 
1.4%
10 5
 
1.4%
ValueCountFrequency (%)
109 1
 
0.3%
29 1
 
0.3%
25 1
 
0.3%
21 1
 
0.3%
19 2
 
0.6%
17 1
 
0.3%
16 1
 
0.3%
15 4
1.2%
14 2
 
0.6%
13 7
2.0%

monster_total_level
Real number (ℝ)

ZEROS 

Distinct90
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.700506
Minimum0
Maximum231.875
Zeros8
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:58.757361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.40625
Q13
median8
Q317
95-th percentile42
Maximum231.875
Range231.875
Interquartile range (IQR)14

Descriptive statistics

Standard deviation20.839314
Coefficient of variation (CV)1.5210617
Kurtosis51.492896
Mean13.700506
Median Absolute Deviation (MAD)5.4375
Skewness5.9227003
Sum4740.375
Variance434.27703
MonotonicityNot monotonic
2024-04-07T10:25:58.856089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 31
 
9.0%
3 22
 
6.4%
4 20
 
5.8%
8 14
 
4.0%
2 14
 
4.0%
10 12
 
3.5%
5 11
 
3.2%
30 11
 
3.2%
12 9
 
2.6%
19 8
 
2.3%
Other values (80) 194
56.1%
ValueCountFrequency (%)
0 8
2.3%
0.125 5
1.4%
0.25 4
1.2%
0.375 1
 
0.3%
0.5 4
1.2%
0.75 2
 
0.6%
0.875 2
 
0.6%
1 6
1.7%
1.125 1
 
0.3%
1.25 2
 
0.6%
ValueCountFrequency (%)
231.875 1
 
0.3%
196 1
 
0.3%
87.125 1
 
0.3%
84 1
 
0.3%
76 1
 
0.3%
66.375 1
 
0.3%
62 1
 
0.3%
59 1
 
0.3%
56.125 1
 
0.3%
56 3
0.9%

party_total_level
Real number (ℝ)

Distinct35
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5057803
Minimum0
Maximum60
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:58.941992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median7
Q312
95-th percentile25
Maximum60
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.5299963
Coefficient of variation (CV)0.89734834
Kurtosis7.7532314
Mean9.5057803
Median Absolute Deviation (MAD)4
Skewness2.3289188
Sum3289
Variance72.760836
MonotonicityNot monotonic
2024-04-07T10:25:59.032573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3 59
17.1%
4 43
12.4%
5 26
 
7.5%
8 24
 
6.9%
6 24
 
6.9%
20 23
 
6.6%
7 23
 
6.6%
12 14
 
4.0%
9 13
 
3.8%
13 12
 
3.5%
Other values (25) 85
24.6%
ValueCountFrequency (%)
0 1
 
0.3%
1 8
 
2.3%
2 8
 
2.3%
3 59
17.1%
4 43
12.4%
5 26
7.5%
6 24
6.9%
7 23
 
6.6%
8 24
6.9%
9 13
 
3.8%
ValueCountFrequency (%)
60 1
0.3%
59 1
0.3%
40 2
0.6%
38 1
0.3%
37 2
0.6%
35 2
0.6%
34 1
0.3%
33 2
0.6%
31 1
0.3%
30 1
0.3%

party_level1_spellslots
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3988439
Minimum0
Maximum20
Zeros135
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:59.112910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile7
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6642885
Coefficient of variation (CV)1.1106552
Kurtosis7.5556788
Mean2.3988439
Median Absolute Deviation (MAD)2
Skewness1.9339836
Sum830
Variance7.0984334
MonotonicityNot monotonic
2024-04-07T10:25:59.197742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 135
39.0%
4 98
28.3%
3 41
 
11.8%
2 26
 
7.5%
1 16
 
4.6%
5 6
 
1.7%
8 5
 
1.4%
7 5
 
1.4%
6 5
 
1.4%
10 2
 
0.6%
Other values (6) 7
 
2.0%
ValueCountFrequency (%)
0 135
39.0%
1 16
 
4.6%
2 26
 
7.5%
3 41
 
11.8%
4 98
28.3%
5 6
 
1.7%
6 5
 
1.4%
7 5
 
1.4%
8 5
 
1.4%
9 1
 
0.3%
ValueCountFrequency (%)
20 1
 
0.3%
15 1
 
0.3%
14 1
 
0.3%
12 1
 
0.3%
11 2
 
0.6%
10 2
 
0.6%
9 1
 
0.3%
8 5
1.4%
7 5
1.4%
6 5
1.4%

party_level2_spellslots
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2687861
Minimum0
Maximum9
Zeros191
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:59.276906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.638312
Coefficient of variation (CV)1.2912437
Kurtosis2.7239695
Mean1.2687861
Median Absolute Deviation (MAD)0
Skewness1.3999692
Sum439
Variance2.6840663
MonotonicityNot monotonic
2024-04-07T10:25:59.344294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 191
55.2%
3 81
23.4%
2 59
 
17.1%
1 4
 
1.2%
7 3
 
0.9%
6 3
 
0.9%
8 2
 
0.6%
5 2
 
0.6%
9 1
 
0.3%
ValueCountFrequency (%)
0 191
55.2%
1 4
 
1.2%
2 59
 
17.1%
3 81
23.4%
5 2
 
0.6%
6 3
 
0.9%
7 3
 
0.9%
8 2
 
0.6%
9 1
 
0.3%
ValueCountFrequency (%)
9 1
 
0.3%
8 2
 
0.6%
7 3
 
0.9%
6 3
 
0.9%
5 2
 
0.6%
3 81
23.4%
2 59
 
17.1%
1 4
 
1.2%
0 191
55.2%

party_level3_spellslots
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70809249
Minimum0
Maximum6
Zeros248
Zeros (%)71.7%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:59.410281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2104824
Coefficient of variation (CV)1.7094976
Kurtosis1.0338714
Mean0.70809249
Median Absolute Deviation (MAD)0
Skewness1.4436622
Sum245
Variance1.4652677
MonotonicityNot monotonic
2024-04-07T10:25:59.478662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 248
71.7%
3 48
 
13.9%
2 38
 
11.0%
1 9
 
2.6%
5 2
 
0.6%
6 1
 
0.3%
ValueCountFrequency (%)
0 248
71.7%
1 9
 
2.6%
2 38
 
11.0%
3 48
 
13.9%
5 2
 
0.6%
6 1
 
0.3%
ValueCountFrequency (%)
6 1
 
0.3%
5 2
 
0.6%
3 48
 
13.9%
2 38
 
11.0%
1 9
 
2.6%
0 248
71.7%

party_level4_spellslots
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34682081
Minimum0
Maximum6
Zeros297
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:59.544885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93619876
Coefficient of variation (CV)2.6993731
Kurtosis7.8189138
Mean0.34682081
Median Absolute Deviation (MAD)0
Skewness2.8173433
Sum120
Variance0.87646812
MonotonicityNot monotonic
2024-04-07T10:25:59.613773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 297
85.8%
3 25
 
7.2%
2 12
 
3.5%
1 10
 
2.9%
6 1
 
0.3%
5 1
 
0.3%
ValueCountFrequency (%)
0 297
85.8%
1 10
 
2.9%
2 12
 
3.5%
3 25
 
7.2%
5 1
 
0.3%
6 1
 
0.3%
ValueCountFrequency (%)
6 1
 
0.3%
5 1
 
0.3%
3 25
 
7.2%
2 12
 
3.5%
1 10
 
2.9%
0 297
85.8%

party_level5_spellslots
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23121387
Minimum0
Maximum5
Zeros312
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:25:59.684939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75661889
Coefficient of variation (CV)3.2723767
Kurtosis11.491609
Mean0.23121387
Median Absolute Deviation (MAD)0
Skewness3.4241422
Sum80
Variance0.57247215
MonotonicityNot monotonic
2024-04-07T10:25:59.756294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 312
90.2%
3 13
 
3.8%
2 13
 
3.8%
1 6
 
1.7%
5 1
 
0.3%
4 1
 
0.3%
ValueCountFrequency (%)
0 312
90.2%
1 6
 
1.7%
2 13
 
3.8%
3 13
 
3.8%
4 1
 
0.3%
5 1
 
0.3%
ValueCountFrequency (%)
5 1
 
0.3%
4 1
 
0.3%
3 13
 
3.8%
2 13
 
3.8%
1 6
 
1.7%
0 312
90.2%

party_level6_spellslots
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
327 
2
 
11
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 327
94.5%
2 11
 
3.2%
1 8
 
2.3%

Length

2024-04-07T10:25:59.839069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:25:59.909487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 327
94.5%
2 11
 
3.2%
1 8
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 327
94.5%
2 11
 
3.2%
1 8
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 327
94.5%
2 11
 
3.2%
1 8
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 327
94.5%
2 11
 
3.2%
1 8
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 327
94.5%
2 11
 
3.2%
1 8
 
2.3%

party_level7_spellslots
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
332 
1
 
8
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 332
96.0%
1 8
 
2.3%
2 6
 
1.7%

Length

2024-04-07T10:25:59.982675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:00.053631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 332
96.0%
1 8
 
2.3%
2 6
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 332
96.0%
1 8
 
2.3%
2 6
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 332
96.0%
1 8
 
2.3%
2 6
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 332
96.0%
1 8
 
2.3%
2 6
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 332
96.0%
1 8
 
2.3%
2 6
 
1.7%

party_level8_spellslots
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
332 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 332
96.0%
1 14
 
4.0%

Length

2024-04-07T10:26:00.130054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:00.196350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 332
96.0%
1 14
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 332
96.0%
1 14
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 332
96.0%
1 14
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 332
96.0%
1 14
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 332
96.0%
1 14
 
4.0%

party_level9_spellslots
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
333 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 333
96.2%
1 13
 
3.8%

Length

2024-04-07T10:26:00.266017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:00.332425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 333
96.2%
1 13
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 333
96.2%
1 13
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 333
96.2%
1 13
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 333
96.2%
1 13
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 333
96.2%
1 13
 
3.8%

party_total_ac
Real number (ℝ)

Distinct50
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.83815
Minimum10
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:00.410527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q116
median18
Q322
95-th percentile52.5
Maximum240
Range230
Interquartile range (IQR)6

Descriptive statistics

Standard deviation17.668849
Coefficient of variation (CV)0.77365499
Kurtosis67.914082
Mean22.83815
Median Absolute Deviation (MAD)3
Skewness6.5332193
Sum7902
Variance312.18822
MonotonicityNot monotonic
2024-04-07T10:26:00.511402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 49
14.2%
16 38
 
11.0%
17 31
 
9.0%
14 29
 
8.4%
19 26
 
7.5%
15 26
 
7.5%
20 22
 
6.4%
13 11
 
3.2%
22 9
 
2.6%
21 9
 
2.6%
Other values (40) 96
27.7%
ValueCountFrequency (%)
10 8
 
2.3%
11 4
 
1.2%
12 5
 
1.4%
13 11
 
3.2%
14 29
8.4%
15 26
7.5%
16 38
11.0%
17 31
9.0%
18 49
14.2%
19 26
7.5%
ValueCountFrequency (%)
240 1
0.3%
102 1
0.3%
85 1
0.3%
81 1
0.3%
78 1
0.3%
76 1
0.3%
74 1
0.3%
72 1
0.3%
65 1
0.3%
64 2
0.6%

party_total_precombat_hp
Real number (ℝ)

Distinct137
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.433526
Minimum8
Maximum679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:00.682452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile21
Q132
median55.5
Q3100
95-th percentile204.75
Maximum679
Range671
Interquartile range (IQR)68

Descriptive statistics

Standard deviation69.033933
Coefficient of variation (CV)0.89152512
Kurtosis17.979239
Mean77.433526
Median Absolute Deviation (MAD)27.5
Skewness3.1126942
Sum26792
Variance4765.684
MonotonicityNot monotonic
2024-04-07T10:26:00.779155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 18
 
5.2%
44 15
 
4.3%
27 13
 
3.8%
31 10
 
2.9%
32 10
 
2.9%
35 8
 
2.3%
86 7
 
2.0%
43 7
 
2.0%
57 7
 
2.0%
66 6
 
1.7%
Other values (127) 245
70.8%
ValueCountFrequency (%)
8 1
 
0.3%
9 2
0.6%
11 2
0.6%
12 2
0.6%
16 1
 
0.3%
17 2
0.6%
18 4
1.2%
20 3
0.9%
21 2
0.6%
22 3
0.9%
ValueCountFrequency (%)
679 1
0.3%
372 1
0.3%
327 1
0.3%
326 1
0.3%
302 1
0.3%
293 1
0.3%
263 1
0.3%
259 1
0.3%
244 1
0.3%
230 1
0.3%

party_total_postcombat_hp
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
345 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 345
99.7%
1 1
 
0.3%

Length

2024-04-07T10:26:00.884031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:00.950481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 345
99.7%
1 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 345
99.7%
1 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 345
99.7%
1 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 345
99.7%
1 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 345
99.7%
1 1
 
0.3%

party_total_hpratio
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0.0
346 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1038
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 346
100.0%

Length

2024-04-07T10:26:01.020523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:01.084016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 346
100.0%

Most occurring characters

ValueCountFrequency (%)
0 692
66.7%
. 346
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 692
66.7%
. 346
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 692
66.7%
. 346
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 692
66.7%
. 346
33.3%

party_total_prof_bonus
Real number (ℝ)

Distinct15
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1791908
Minimum2
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:01.143442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q35
95-th percentile10
Maximum28
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0516758
Coefficient of variation (CV)0.73020735
Kurtosis14.355023
Mean4.1791908
Median Absolute Deviation (MAD)1
Skewness3.0739406
Sum1446
Variance9.3127251
MonotonicityNot monotonic
2024-04-07T10:26:01.217654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 114
32.9%
3 77
22.3%
4 51
14.7%
6 43
 
12.4%
5 27
 
7.8%
12 8
 
2.3%
9 8
 
2.3%
8 4
 
1.2%
15 3
 
0.9%
7 3
 
0.9%
Other values (5) 8
 
2.3%
ValueCountFrequency (%)
2 114
32.9%
3 77
22.3%
4 51
14.7%
5 27
 
7.8%
6 43
 
12.4%
7 3
 
0.9%
8 4
 
1.2%
9 8
 
2.3%
10 3
 
0.9%
12 8
 
2.3%
ValueCountFrequency (%)
28 1
 
0.3%
18 2
 
0.6%
17 1
 
0.3%
15 3
 
0.9%
13 1
 
0.3%
12 8
2.3%
10 3
 
0.9%
9 8
2.3%
8 4
1.2%
7 3
 
0.9%

party_total_strength
Real number (ℝ)

Distinct44
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.271676
Minimum6
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:01.299741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median16
Q319
95-th percentile39.5
Maximum175
Range169
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.422938
Coefficient of variation (CV)0.73463088
Kurtosis55.898876
Mean18.271676
Median Absolute Deviation (MAD)4
Skewness5.804553
Sum6322
Variance180.17525
MonotonicityNot monotonic
2024-04-07T10:26:01.407352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
18 42
12.1%
16 34
 
9.8%
15 30
 
8.7%
20 24
 
6.9%
10 23
 
6.6%
8 22
 
6.4%
12 21
 
6.1%
9 21
 
6.1%
13 18
 
5.2%
14 17
 
4.9%
Other values (34) 94
27.2%
ValueCountFrequency (%)
6 2
 
0.6%
7 1
 
0.3%
8 22
6.4%
9 21
6.1%
10 23
6.6%
11 15
4.3%
12 21
6.1%
13 18
5.2%
14 17
4.9%
15 30
8.7%
ValueCountFrequency (%)
175 1
0.3%
75 1
0.3%
70 1
0.3%
67 1
0.3%
65 1
0.3%
63 1
0.3%
62 1
0.3%
57 1
0.3%
55 1
0.3%
51 1
0.3%

party_total_dexterity
Real number (ℝ)

Distinct50
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.809249
Minimum6
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:01.508693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q114
median16
Q320
95-th percentile49.75
Maximum208
Range202
Interquartile range (IQR)6

Descriptive statistics

Standard deviation16.778991
Coefficient of variation (CV)0.80632371
Kurtosis48.325831
Mean20.809249
Median Absolute Deviation (MAD)3
Skewness5.4991603
Sum7200
Variance281.53452
MonotonicityNot monotonic
2024-04-07T10:26:01.616060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 55
15.9%
16 43
12.4%
20 39
11.3%
18 23
 
6.6%
10 18
 
5.2%
12 18
 
5.2%
17 16
 
4.6%
13 15
 
4.3%
11 14
 
4.0%
19 12
 
3.5%
Other values (40) 93
26.9%
ValueCountFrequency (%)
6 4
 
1.2%
7 3
 
0.9%
8 2
 
0.6%
9 3
 
0.9%
10 18
 
5.2%
11 14
 
4.0%
12 18
 
5.2%
13 15
 
4.3%
14 55
15.9%
15 12
 
3.5%
ValueCountFrequency (%)
208 1
0.3%
110 1
0.3%
93 1
0.3%
81 1
0.3%
78 1
0.3%
76 1
0.3%
67 1
0.3%
66 1
0.3%
63 1
0.3%
62 1
0.3%

party_total_constitution
Real number (ℝ)

Distinct43
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.858382
Minimum10
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:01.712987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q114
median16
Q319.75
95-th percentile46.75
Maximum198
Range188
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation15.430072
Coefficient of variation (CV)0.73975406
Kurtosis52.931693
Mean20.858382
Median Absolute Deviation (MAD)2
Skewness5.7757006
Sum7217
Variance238.08713
MonotonicityNot monotonic
2024-04-07T10:26:01.811901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16 63
18.2%
14 58
16.8%
18 33
9.5%
20 25
 
7.2%
15 21
 
6.1%
17 20
 
5.8%
12 20
 
5.8%
19 18
 
5.2%
13 16
 
4.6%
30 8
 
2.3%
Other values (33) 64
18.5%
ValueCountFrequency (%)
10 8
 
2.3%
11 2
 
0.6%
12 20
 
5.8%
13 16
 
4.6%
14 58
16.8%
15 21
 
6.1%
16 63
18.2%
17 20
 
5.8%
18 33
9.5%
19 18
 
5.2%
ValueCountFrequency (%)
198 1
 
0.3%
91 1
 
0.3%
80 1
 
0.3%
77 1
 
0.3%
76 1
 
0.3%
75 2
0.6%
63 1
 
0.3%
59 1
 
0.3%
56 3
0.9%
55 1
 
0.3%

party_total_intelligence
Real number (ℝ)

Distinct42
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.352601
Minimum5
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:01.906611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q110
median12
Q318
95-th percentile39
Maximum155
Range150
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.916564
Coefficient of variation (CV)0.78987826
Kurtosis41.507717
Mean16.352601
Median Absolute Deviation (MAD)2
Skewness5.0868528
Sum5658
Variance166.83763
MonotonicityNot monotonic
2024-04-07T10:26:02.003505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
12 47
13.6%
10 43
12.4%
11 38
11.0%
8 36
10.4%
13 26
 
7.5%
14 21
 
6.1%
16 16
 
4.6%
20 15
 
4.3%
9 14
 
4.0%
19 11
 
3.2%
Other values (32) 79
22.8%
ValueCountFrequency (%)
5 1
 
0.3%
7 3
 
0.9%
8 36
10.4%
9 14
 
4.0%
10 43
12.4%
11 38
11.0%
12 47
13.6%
13 26
7.5%
14 21
6.1%
15 5
 
1.4%
ValueCountFrequency (%)
155 1
0.3%
78 1
0.3%
69 1
0.3%
64 1
0.3%
63 1
0.3%
60 2
0.6%
55 1
0.3%
54 1
0.3%
50 1
0.3%
47 2
0.6%

party_total_wisdom
Real number (ℝ)

Distinct45
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.239884
Minimum7
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:02.102215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10
Q113
median15
Q319
95-th percentile44
Maximum175
Range168
Interquartile range (IQR)6

Descriptive statistics

Standard deviation14.513671
Coefficient of variation (CV)0.75435332
Kurtosis42.008834
Mean19.239884
Median Absolute Deviation (MAD)3
Skewness5.1440363
Sum6657
Variance210.64664
MonotonicityNot monotonic
2024-04-07T10:26:02.200418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
14 48
13.9%
16 48
13.9%
13 39
11.3%
12 26
 
7.5%
11 24
 
6.9%
10 21
 
6.1%
20 19
 
5.5%
18 15
 
4.3%
15 15
 
4.3%
17 11
 
3.2%
Other values (35) 80
23.1%
ValueCountFrequency (%)
7 1
 
0.3%
8 5
 
1.4%
9 1
 
0.3%
10 21
6.1%
11 24
6.9%
12 26
7.5%
13 39
11.3%
14 48
13.9%
15 15
 
4.3%
16 48
13.9%
ValueCountFrequency (%)
175 1
0.3%
90 1
0.3%
88 1
0.3%
81 1
0.3%
63 2
0.6%
62 1
0.3%
61 1
0.3%
60 1
0.3%
57 1
0.3%
52 1
0.3%

party_total_charisma
Real number (ℝ)

Distinct48
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.927746
Minimum6
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:02.299463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q111
median13
Q320
95-th percentile41
Maximum175
Range169
Interquartile range (IQR)9

Descriptive statistics

Standard deviation14.585996
Coefficient of variation (CV)0.81359902
Kurtosis42.55987
Mean17.927746
Median Absolute Deviation (MAD)3
Skewness5.1502612
Sum6203
Variance212.75129
MonotonicityNot monotonic
2024-04-07T10:26:02.397963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
12 41
11.8%
11 34
 
9.8%
13 31
 
9.0%
10 30
 
8.7%
20 29
 
8.4%
8 26
 
7.5%
18 23
 
6.6%
15 17
 
4.9%
14 16
 
4.6%
16 13
 
3.8%
Other values (38) 86
24.9%
ValueCountFrequency (%)
6 2
 
0.6%
7 3
 
0.9%
8 26
7.5%
9 11
 
3.2%
10 30
8.7%
11 34
9.8%
12 41
11.8%
13 31
9.0%
14 16
 
4.6%
15 17
4.9%
ValueCountFrequency (%)
175 1
0.3%
91 1
0.3%
85 1
0.3%
77 1
0.3%
73 1
0.3%
64 1
0.3%
60 1
0.3%
55 2
0.6%
52 1
0.3%
51 1
0.3%

player_monster_ratio
Real number (ℝ)

Distinct42
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72058218
Minimum0.027522936
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:02.496122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.027522936
5-th percentile0.090909091
Q10.27597403
median1
Q31
95-th percentile1
Maximum4
Range3.9724771
Interquartile range (IQR)0.72402597

Descriptive statistics

Standard deviation0.52296836
Coefficient of variation (CV)0.72575812
Kurtosis6.6428976
Mean0.72058218
Median Absolute Deviation (MAD)0.5
Skewness1.6937421
Sum249.32143
Variance0.27349591
MonotonicityNot monotonic
2024-04-07T10:26:02.595919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 161
46.5%
0.5 39
 
11.3%
0.3333333333 26
 
7.5%
0.2 19
 
5.5%
0.25 14
 
4.0%
2 10
 
2.9%
0.1666666667 10
 
2.9%
0.1428571429 8
 
2.3%
0.1111111111 5
 
1.4%
0.125 4
 
1.2%
Other values (32) 50
 
14.5%
ValueCountFrequency (%)
0.027522936 1
 
0.3%
0.034482759 1
 
0.3%
0.047619048 1
 
0.3%
0.052631579 1
 
0.3%
0.058823529 1
 
0.3%
0.0625 1
 
0.3%
0.066666667 3
0.9%
0.071428571 1
 
0.3%
0.076923077 4
1.2%
0.083333333 2
0.6%
ValueCountFrequency (%)
4 1
 
0.3%
3 3
 
0.9%
2.5 1
 
0.3%
2 10
 
2.9%
1.5 1
 
0.3%
1.333333333 1
 
0.3%
1 161
46.5%
0.75 2
 
0.6%
0.6666666667 4
 
1.2%
0.625 1
 
0.3%

monster_player_ratio
Real number (ℝ)

Distinct42
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0845376
Minimum0.25
Maximum36.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:02.691027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile1
Q11
median1
Q33.625
95-th percentile11
Maximum36.333333
Range36.083333
Interquartile range (IQR)2.625

Descriptive statistics

Standard deviation4.0277785
Coefficient of variation (CV)1.3057965
Kurtosis20.103652
Mean3.0845376
Median Absolute Deviation (MAD)0.5
Skewness3.7206495
Sum1067.25
Variance16.222999
MonotonicityNot monotonic
2024-04-07T10:26:02.786683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 161
46.5%
2 39
 
11.3%
3 26
 
7.5%
5 19
 
5.5%
4 14
 
4.0%
0.5 10
 
2.9%
6 10
 
2.9%
7 8
 
2.3%
9 5
 
1.4%
8 4
 
1.2%
Other values (32) 50
 
14.5%
ValueCountFrequency (%)
0.25 1
 
0.3%
0.3333333333 3
 
0.9%
0.4 1
 
0.3%
0.5 10
 
2.9%
0.6666666667 1
 
0.3%
0.75 1
 
0.3%
1 161
46.5%
1.333333333 2
 
0.6%
1.5 4
 
1.2%
1.6 1
 
0.3%
ValueCountFrequency (%)
36.33333333 1
 
0.3%
29 1
 
0.3%
21 1
 
0.3%
19 1
 
0.3%
17 1
 
0.3%
16 1
 
0.3%
15 3
0.9%
14 1
 
0.3%
13 4
1.2%
12 2
0.6%

Blood Hunter
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
326 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Length

2024-04-07T10:26:02.877049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.024471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Ranger
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
305 
1
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 305
88.2%
1 41
 
11.8%

Length

2024-04-07T10:26:03.100158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.167325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 305
88.2%
1 41
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 305
88.2%
1 41
 
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 305
88.2%
1 41
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 305
88.2%
1 41
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 305
88.2%
1 41
 
11.8%

Bard
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
326 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Length

2024-04-07T10:26:03.238199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.304332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 326
94.2%
1 20
 
5.8%

Rogue
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
298 
1
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 298
86.1%
1 48
 
13.9%

Length

2024-04-07T10:26:03.373437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.439770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 298
86.1%
1 48
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 298
86.1%
1 48
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 298
86.1%
1 48
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 298
86.1%
1 48
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 298
86.1%
1 48
 
13.9%

Warlock
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
291 
1
55 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291
84.1%
1 55
 
15.9%

Length

2024-04-07T10:26:03.510944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.576368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 291
84.1%
1 55
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 291
84.1%
1 55
 
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 291
84.1%
1 55
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 291
84.1%
1 55
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 291
84.1%
1 55
 
15.9%

Wizard
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
296 
1
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 296
85.5%
1 50
 
14.5%

Length

2024-04-07T10:26:03.648849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.714729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 296
85.5%
1 50
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 296
85.5%
1 50
 
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 296
85.5%
1 50
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 296
85.5%
1 50
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 296
85.5%
1 50
 
14.5%

Druid
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
325 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 325
93.9%
1 21
 
6.1%

Length

2024-04-07T10:26:03.787198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.852387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 325
93.9%
1 21
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 325
93.9%
1 21
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 325
93.9%
1 21
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 325
93.9%
1 21
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 325
93.9%
1 21
 
6.1%

Monk
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
306 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 306
88.4%
1 40
 
11.6%

Length

2024-04-07T10:26:03.922915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:03.988728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 306
88.4%
1 40
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 306
88.4%
1 40
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 306
88.4%
1 40
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 306
88.4%
1 40
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 306
88.4%
1 40
 
11.6%

Paladin
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
304 
1
42 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 304
87.9%
1 42
 
12.1%

Length

2024-04-07T10:26:04.059448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:04.129168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 304
87.9%
1 42
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 304
87.9%
1 42
 
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 304
87.9%
1 42
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 304
87.9%
1 42
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 304
87.9%
1 42
 
12.1%

Fighter
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
261 
1
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 261
75.4%
1 85
 
24.6%

Length

2024-04-07T10:26:04.199776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:04.268074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 261
75.4%
1 85
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 261
75.4%
1 85
 
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 261
75.4%
1 85
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 261
75.4%
1 85
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 261
75.4%
1 85
 
24.6%

Barbarian
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
294 
1
52 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294
85.0%
1 52
 
15.0%

Length

2024-04-07T10:26:04.339407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:04.406129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 294
85.0%
1 52
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 294
85.0%
1 52
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 294
85.0%
1 52
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 294
85.0%
1 52
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 294
85.0%
1 52
 
15.0%

Cleric
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
280 
1
66 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 280
80.9%
1 66
 
19.1%

Length

2024-04-07T10:26:04.478657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:04.543722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 280
80.9%
1 66
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 280
80.9%
1 66
 
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 280
80.9%
1 66
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 280
80.9%
1 66
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 280
80.9%
1 66
 
19.1%

Sorcerer
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
311 
1
35 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 311
89.9%
1 35
 
10.1%

Length

2024-04-07T10:26:04.616303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:26:04.681708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 311
89.9%
1 35
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 311
89.9%
1 35
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 311
89.9%
1 35
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 311
89.9%
1 35
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 311
89.9%
1 35
 
10.1%

weighted_monster_level
Real number (ℝ)

ZEROS 

Distinct115
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.957009
Minimum0
Maximum927.5
Zeros8
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:04.765078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q14.53125
median11
Q326.1875
95-th percentile107.25
Maximum927.5
Range927.5
Interquartile range (IQR)21.65625

Descriptive statistics

Standard deviation67.303658
Coefficient of variation (CV)2.4967035
Kurtosis106.90596
Mean26.957009
Median Absolute Deviation (MAD)8
Skewness9.1108818
Sum9327.125
Variance4529.7823
MonotonicityNot monotonic
2024-04-07T10:26:04.857019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 32
 
9.2%
3 25
 
7.2%
30 20
 
5.8%
4 13
 
3.8%
12 13
 
3.8%
5 11
 
3.2%
8 10
 
2.9%
2 10
 
2.9%
13 9
 
2.6%
10 9
 
2.6%
Other values (105) 194
56.1%
ValueCountFrequency (%)
0 8
2.3%
0.125 5
1.4%
0.25 2
 
0.6%
0.375 2
 
0.6%
0.5 2
 
0.6%
0.75 2
 
0.6%
1 7
2.0%
1.5 2
 
0.6%
1.75 1
 
0.3%
2 10
2.9%
ValueCountFrequency (%)
927.5 1
0.3%
588 1
0.3%
261.375 1
0.3%
218.5 1
0.3%
199.125 1
0.3%
190 1
0.3%
168.375 1
0.3%
168 1
0.3%
147.5 1
0.3%
144 1
0.3%

weighted_spell_slots
Real number (ℝ)

ZEROS 

Distinct81
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.60694
Minimum0
Maximum1382
Zeros113
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-07T10:26:04.945590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q3122
95-th percentile467.75
Maximum1382
Range1382
Interquartile range (IQR)122

Descriptive statistics

Standard deviation209.40193
Coefficient of variation (CV)1.9280714
Kurtosis12.090116
Mean108.60694
Median Absolute Deviation (MAD)30
Skewness3.3460203
Sum37578
Variance43849.167
MonotonicityNot monotonic
2024-04-07T10:26:05.042712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 113
32.7%
58 25
 
7.2%
18 22
 
6.4%
12 18
 
5.2%
139 13
 
3.8%
6 13
 
3.8%
75 10
 
2.9%
34 8
 
2.3%
64 7
 
2.0%
30 6
 
1.7%
Other values (71) 111
32.1%
ValueCountFrequency (%)
0 113
32.7%
6 13
 
3.8%
12 18
 
5.2%
18 22
 
6.4%
24 1
 
0.3%
29 1
 
0.3%
30 6
 
1.7%
34 8
 
2.3%
35 2
 
0.6%
46 2
 
0.6%
ValueCountFrequency (%)
1382 1
 
0.3%
1034 4
1.2%
1028 1
 
0.3%
979 1
 
0.3%
951 3
0.9%
919 1
 
0.3%
878 2
0.6%
709 1
 
0.3%
584 1
 
0.3%
504 2
0.6%

Interactions

2024-04-07T10:25:50.486048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:12.192855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.079188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.657212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.257878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.756701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.374736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:22.020555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:24.031612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:26.050116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.617638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.656908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.200201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.838734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.612374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.686872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.688401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.382667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:42.058184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.612606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.681961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.316361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.992460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.561353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:12.293982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.153287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.733174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.330911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.832830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.452041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:22.172219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:24.104593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:26.123862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.693606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.728871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.277399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.908272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.688256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.765830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.762959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.458878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:42.131715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.685872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.760751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.391610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:49.063885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.630990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:12.367907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.220389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.800816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.395424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.900613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.524317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:22.235152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:24.169970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:26.191161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.760058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.793496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.346393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.972068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.760047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.882961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.830471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.527902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:42.198797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.754394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.832709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.461049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:49.129879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.694040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:12.435816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.287294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.860185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.455798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.964833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.589667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:22.294340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:24.231897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:26.254507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.821767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.884970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.408872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:33.030136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.838883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.956684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.894358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.592972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:42.274396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.818042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.899534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.524568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:49.189783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.757055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:12.501465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.347694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.923990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.512130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:19.026083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.655549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:22.353727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:24.290437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:26.315922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.881715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.948061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.471485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:33.087411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.934670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:37.218473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.958703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.658928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:42.335694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.885279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.963329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.586729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:49.249382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.824130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:12.571688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.416125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.988327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.577512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:19.092701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.726718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:22.416064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:24.355999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:26.380312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.946766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:30.019118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.537650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:33.251481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:35.005274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:37.379716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:39.024821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.728859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:42.402023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.959136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:46.033065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.652948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:49.314279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.898645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:12.648430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.488048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:16.061691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.646721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:19.163471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.800839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:22.501070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:25:15.090495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:16.720806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.212832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:19.826401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.428098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.496479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.288857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.065560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:28.865276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:30.668438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.285031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:33.983904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.033110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.123765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:39.826645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.411508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.068895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:44.981634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:46.738034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.437299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:49.951783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:51.648126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:13.560031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.162288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:16.787690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.280846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:19.899132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.503042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.571530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.387911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.134768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:28.933167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:30.737143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.353987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.057749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.126624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.194812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:39.896555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.483133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.135944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.053901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:46.812266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.506650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.019953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:51.723674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:13.635946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.233932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:16.857801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.350058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:19.968269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.578067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.644634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.460156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.206691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.004988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:30.804555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.426400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.128869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.199335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.266911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:39.967937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.553647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.207621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.129162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:46.887235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.579060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.088775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:51.794476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:13.713210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.303124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:16.925012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.416531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.037531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.652272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.708575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.529781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.274444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.078753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:30.872491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.494972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.197793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.271261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.339243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.036585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.625698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.273941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.326986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:46.957500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.646921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.156780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:51.866189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:13.786349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.373075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:16.992909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.489657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.105000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.728340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.772755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.600558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.344502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.252172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:30.937728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.562565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.318884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.385588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.409182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.104846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.694949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.342719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.402299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.029946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.716885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.223574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:51.939688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:13.863759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.450010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.063279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.559291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.176650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.806049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.841440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.672880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.417222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.324237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.010237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.635740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.395833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.476283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.484065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.178057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.769727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.414890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.479799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.104490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.789904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.293390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:52.012172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:13.937518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.523093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.130997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.626168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.243787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.879825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.905909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.742613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.487374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.527886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.075702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.705854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.476919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.551981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.554226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.248579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.845901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.482961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.548691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.178441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.859591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.362086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:52.075014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:14.007020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:15.586694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:17.190136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:18.690865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:20.307882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:21.946498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:23.964492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:25.803513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:27.549881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:29.588970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:31.134878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:32.767763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:34.541973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:36.616164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:38.618059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:40.313016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:41.990214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:43.543592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:45.612046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:47.243366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:48.921806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:25:50.418254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-04-07T10:25:52.255219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-07T10:25:52.519691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

combat_idstart_timeplayer_idsplayer_infomonsters_infoparty_sizetotal_slotstotal_max_slotsparty_classes_with_levelparty_total_class_compositionplayer_individual_hp_ratiosplayer_individual_acplayer_individual_prof_bonusplayer_individual_strengthplayer_individual_dexterityplayer_individual_constitutionplayer_individual_intelligenceplayer_individual_wisdomplayer_individual_charismamonster_typesmonster_numbermonster_total_levelparty_total_levelparty_level1_spellslotsparty_level2_spellslotsparty_level3_spellslotsparty_level4_spellslotsparty_level5_spellslotsparty_level6_spellslotsparty_level7_spellslotsparty_level8_spellslotsparty_level9_spellslotsparty_total_acparty_total_precombat_hpparty_total_postcombat_hpparty_total_hpratioparty_total_prof_bonusparty_total_strengthparty_total_dexterityparty_total_constitutionparty_total_intelligenceparty_total_wisdomparty_total_charismaplayer_monster_ratiomonster_player_ratioBlood HunterRangerBardRogueWarlockWizardDruidMonkPaladinFighterBarbarianClericSorcererweighted_monster_levelweighted_spell_slots
01667093704-73130db4-c26b-4778-a242-0b98c46adabc1.667094e+09['177558997848408361'][{'hp_ratio': (0, 23), 'class': [('Warlock', 4)], 'slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 2, 'strength': 20, 'dexterity': 9, 'constitution': 10, 'intelligence': 12, 'wisdom': 14, 'charisma': 19}}][{'monster_id': '4c7d90bb-9255-44a9-aa4e-f6ffa1fa9000', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'b5491d16-ca83-4996-82d1-41d1ec1b7db6', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '6ede1067-ed54-42bb-9ce2-14068477164c', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '15406585-e135-4bde-b13a-95e9039052c4', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'cba094e4-cbda-4cb3-b96d-6783a378cba9', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '535a9ef0-3c3c-4719-824c-761ee6ba0675', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': '18ae3599-3f5f-434c-abf4-08985400956c', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5b665adc-16fb-4a81-93da-3476c692d470', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5369b5d1-45e9-4242-8a9b-1d4280822a1e', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5a49cbdf-9890-4826-bb02-98a9f60cf367', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'f752f71e-0840-4f4c-af8e-73cd169acf8a', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'ff186863-f2ab-40f0-b194-8e2f4965b444', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '62baee43-b320-414a-b2fb-1010a1635bf0', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '0162fa37-9f5a-4be5-b820-437ef078ae4e', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'd1084b11-b70b-4b0b-826d-ef22e6237863', 'monster_code': 'SoR2', 'monster_name': 'Swarm of Rats', 'level': 0.25}]1{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Warlock', 4)]['Warlock'][(0, 23)][19][2][20][9][10][12][14][19]['Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Swarm of Rats']152.3754020000000192300.02209101214190.06666715.00000000001000000009.534
11658885575-ee4d97ac-453a-4de1-ac3d-a10dc227bb921.658886e+09['127965999109502658'][{'hp_ratio': (0, 75), 'class': [('Bard', 8)], 'slots': {'1': 4, '2': 1, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 7, 'constitution': 18, 'intelligence': 15, 'wisdom': 15, 'charisma': 17}}][{'monster_id': 'c20a1c98-e82c-4906-893d-0a9ebb2ba3ef', 'monster_code': 'HO1', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': 'e074b563-a6be-4b87-8532-ae0b2782bbb9', 'monster_code': 'HO2', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '6d0a08d1-c8f0-445d-a510-a36e343710d8', 'monster_code': 'HO3', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '1b3b4139-47a2-45eb-a3cf-a3fd0f2f9bfa', 'monster_code': 'HO4', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': 'cdcfd1ca-1dcc-460d-a2e3-374badb50c35', 'monster_code': 'HO5', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '5561f699-49ad-4b38-83f4-c7cd88548c74', 'monster_code': 'HO6', 'monster_name': 'Half-Ogre', 'level': 1.0}]1{'1': 4, '2': 1, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Bard', 8)]['Bard'][(0, 75)][19][3][19][7][18][15][15][17]['Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre']66.0008413200000197500.03197181515170.1666676.000000001000000000012.0225
21664367987-7e065799-4cb1-4646-94a9-ad201547e4431.664368e+09['326793710699339197', '264022841131784635', '239987723820313556'][{'hp_ratio': None, 'class': [('Rogue', 9)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 4, 'strength': 19, 'dexterity': 20, 'constitution': 16, 'intelligence': 14, 'wisdom': 10, 'charisma': 10}}, {'hp_ratio': None, 'class': [('Blood Hunter', 9)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 20, 'stats': {'prof_bonus': 4, 'strength': 21, 'dexterity': 14, 'constitution': 20, 'intelligence': 11, 'wisdom': 16, 'charisma': 8}}, {'hp_ratio': (0, 81), 'class': [('Warlock', 13)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 5, 'strength': 15, 'dexterity': 18, 'constitution': 12, 'intelligence': 17, 'wisdom': 11, 'charisma': 13}}][{'monster_id': '89bca030-87a3-47a3-9621-032fe23ba0a8', 'monster_code': 'DR2', 'monster_name': 'Drow', 'level': 0.25}, {'monster_id': 'e3ab898a-4f96-4cd4-9f8a-fa66394a7b14', 'monster_code': 'DR1', 'monster_name': 'Drow', 'level': 0.25}, {'monster_id': 'f028f6c8-3811-4a6e-bce0-3178cfcb9e9c', 'monster_code': 'KN1', 'monster_name': 'Knight', 'level': 3.0}, {'monster_id': '08ccd782-f99c-449b-9bbc-7441d5738c7a', 'monster_code': 'KN1', 'monster_name': 'Knight', 'level': 3.0}]3{'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}[('Rogue', 9), ('Blood Hunter', 9), ('Warlock', 13)]['Rogue', 'Blood Hunter', 'Warlock'][(0, 81)][17, 20, 16][4, 4, 5][19, 21, 15][20, 14, 18][16, 20, 12][14, 11, 17][10, 16, 11][10, 8, 13]['Drow', 'Drow', 'Knight', 'Knight']46.50031000030000538100.0135552484237310.7500001.333333100110000000013.0192
31664333782-19e43046-cc43-4927-8570-152502d1275d1.664334e+09['288900826835560174'][{'hp_ratio': (0, 114), 'class': [('Fighter', 11)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 4, 'strength': 20, 'dexterity': 10, 'constitution': 19, 'intelligence': 12, 'wisdom': 12, 'charisma': 12}}][{'monster_id': '51ceaa89-0155-46e4-9406-58a4c8d0a4ad', 'monster_code': 'FDT1', 'monster_name': 'Forsaken Dire Troll', 'level': 15.0}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Fighter', 11)]['Fighter'][(0, 114)][19][4][20][10][19][12][12][12]['Forsaken Dire Troll']115.000110000000001911400.042010191212121.0000001.000000000000000100015.00
41665884477-91457c2f-4cde-4066-b9c4-77d8982d862e1.665884e+09['163631059791733886'][{'hp_ratio': (0, 48), 'class': [('Cleric', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 3, 'strength': 16, 'dexterity': 14, 'constitution': 16, 'intelligence': 10, 'wisdom': 16, 'charisma': 12}}][{'monster_id': 'd9d0a661-232b-4db3-83d3-960b6c78f1ba', 'monster_code': 'HC1', 'monster_name': 'Hobgoblin Captain', 'level': 3.0}, {'monster_id': 'aebce6db-abbe-4c08-94bf-e8cf24b529b6', 'monster_code': 'HO1', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '24662a24-d25f-4044-b37b-e901f7e242b0', 'monster_code': 'HO2', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'f85a3e03-ffa8-44e0-b4f6-c28e1442ec6e', 'monster_code': 'HO5', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'ce37caa6-688f-4a32-b421-eaf3a42f37bc', 'monster_code': 'HO3', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'aed813cc-0907-487a-b51f-65b3b26fb096', 'monster_code': 'HO6', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '3be5a1e2-9f5e-4ee2-b318-90c0431f0b03', 'monster_code': 'HO7', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '57a6b71b-e49b-45ee-9b4a-38b9e9f07002', 'monster_code': 'HO8', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': 'e57987d8-2069-4f9c-8630-1927b99f9221', 'monster_code': 'HO4', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '22f1665b-687a-4d5c-89b3-ceccb4b8223a', 'monster_code': 'HO9', 'monster_name': 'Hobgoblin', 'level': 0.5}, {'monster_id': '7fa63916-0a40-455a-b19b-5739d049e1ea', 'monster_code': 'GB1', 'monster_name': 'Giant Boar', 'level': 2.0}]1{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Cleric', 5)]['Cleric'][(0, 48)][16][3][16][14][16][10][16][12]['Hobgoblin Captain', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Hobgoblin', 'Giant Boar']119.5005432000000164800.031614161016120.09090911.000000000000000001028.5139
51666298872-b16d909c-53c6-4bfd-bcc0-f045390a26811.666299e+09['240184232026852789'][{'hp_ratio': (0, 224), 'class': [('Fighter', 20)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 25, 'stats': {'prof_bonus': 6, 'strength': 22, 'dexterity': 13, 'constitution': 20, 'intelligence': 10, 'wisdom': 11, 'charisma': 10}}][{'monster_id': 'd0947ff3-cdb6-4bdd-abf0-43cf93350667', 'monster_code': 'SC1', 'monster_name': 'Scarlet', 'level': 19.0}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Fighter', 20)]['Fighter'][(0, 224)][25][6][22][13][20][10][11][10]['Scarlet']119.000200000000002522400.062213201011101.0000001.000000000000000100019.00
61658768918-afcaaf67-a36f-4a62-acfb-23d9550c53161.658769e+09['129705414623236906', '252085070492554802', '127319176105685816'][{'hp_ratio': (0, 31), 'class': [('Druid', 4)], 'slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 11, 'stats': {'prof_bonus': 2, 'strength': 15, 'dexterity': 11, 'constitution': 14, 'intelligence': 15, 'wisdom': 17, 'charisma': 11}}, {'hp_ratio': (0, 74), 'class': [('Ranger', 10)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 4, 'strength': 19, 'dexterity': 18, 'constitution': 13, 'intelligence': 13, 'wisdom': 16, 'charisma': 12}}, {'hp_ratio': None, 'class': [('Sorcerer', 17), ('Warlock', 2)], 'slots': {'1': 6, '2': 3, '3': 3, '4': 2, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}, 'max_slots': {'1': 6, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}, 'ac': 25, 'stats': {'prof_bonus': 6, 'strength': 9, 'dexterity': 14, 'constitution': 16, 'intelligence': 11, 'wisdom': 13, 'charisma': 20}}][{'monster_id': 'cfe87766-77d5-4b09-bbd8-f682860a2244', 'monster_code': 'Bakari', 'monster_name': 'Adult Bakari', 'level': 14.0}]3{'1': 14, '2': 8, '3': 5, '4': 2, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}{'1': 14, '2': 9, '3': 5, '4': 3, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}[('Druid', 4), ('Ranger', 10), ('Sorcerer', 17), ('Warlock', 2)]['Druid', 'Ranger', 'Sorcerer', 'Warlock'][(0, 31), (0, 74)][11, 17, 25][2, 4, 6][15, 19, 9][11, 18, 14][14, 13, 16][15, 13, 11][17, 16, 13][11, 12, 20]['Adult Bakari']114.0003314852211115310500.0124343433946433.0000000.333333010010100000114.0979
71658195387-67ab61f9-0167-4831-8b97-b4b9b1cecdd91.658195e+09['971548668218069599'][{'hp_ratio': (0, 78), 'class': [('Barbarian', 1), ('Paladin', 7)], 'slots': {'1': 3, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 15, 'stats': {'prof_bonus': 3, 'strength': 18, 'dexterity': 14, 'constitution': 16, 'intelligence': 8, 'wisdom': 8, 'charisma': 13}}][{'monster_id': '4debf287-02b2-48d7-a686-ce46a1fb57a7', 'monster_code': 'Elmer3', 'monster_name': 'Gladiator', 'level': 5.0}, {'monster_id': '26682e4b-08fa-4d2f-8cd2-c4cd7e0e9fc9', 'monster_code': 'Elmer2', 'monster_name': 'Gladiator', 'level': 5.0}, {'monster_id': '9769f971-f70f-4fee-b7b7-c6b7fd02c6ba', 'monster_code': 'Elmer1', 'monster_name': 'Gladiator', 'level': 5.0}]1{'1': 3, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Barbarian', 1), ('Paladin', 7)]['Barbarian', 'Paladin'][(0, 78)][15][3][18][14][16][8][8][13]['Gladiator', 'Gladiator', 'Gladiator']315.0008320000000157800.0318141688130.3333333.000000000000001010030.052
81661531195-75b17922-a46a-4f25-8443-774d05ae2aed1.661531e+09['532851975771166592'][{'hp_ratio': (0, 57), 'class': [('Barbarian', 4)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 2, 'strength': 20, 'dexterity': 14, 'constitution': 18, 'intelligence': 7, 'wisdom': 12, 'charisma': 10}}][{'monster_id': 'd24e1f26-c99c-485d-a39c-ca50fb5dfa12', 'monster_code': 'KE1', 'monster_name': 'Kelpie', 'level': 4.0}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Barbarian', 4)]['Barbarian'][(0, 57)][17][2][20][14][18][7][12][10]['Kelpie']14.0004000000000175700.02201418712101.0000001.00000000000000001004.00
91658988238-60a0c6dd-9a56-4c8b-9bee-79d4839b7cf41.658988e+09['137095815938321986'][{'hp_ratio': (0, 111), 'class': [('Warlock', 11), ('Sorcerer', 1)], 'slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 22, 'stats': {'prof_bonus': 4, 'strength': 14, 'dexterity': 14, 'constitution': 19, 'intelligence': 10, 'wisdom': 16, 'charisma': 18}}][{'monster_id': '2f14333e-5296-4e50-83f9-dc8ad5c7dda2', 'monster_code': 'Bakari', 'monster_name': 'Adult Bakari', 'level': 14.0}]1{'1': 2, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 2, '2': 0, '3': 0, '4': 0, '5': 3, '6': 0, '7': 0, '8': 0, '9': 0}[('Warlock', 11), ('Sorcerer', 1)]['Warlock', 'Sorcerer'][(0, 111)][22][4][14][14][19][10][16][18]['Adult Bakari']114.000122000300002211100.041414191016181.0000001.000000000010000000114.0204
combat_idstart_timeplayer_idsplayer_infomonsters_infoparty_sizetotal_slotstotal_max_slotsparty_classes_with_levelparty_total_class_compositionplayer_individual_hp_ratiosplayer_individual_acplayer_individual_prof_bonusplayer_individual_strengthplayer_individual_dexterityplayer_individual_constitutionplayer_individual_intelligenceplayer_individual_wisdomplayer_individual_charismamonster_typesmonster_numbermonster_total_levelparty_total_levelparty_level1_spellslotsparty_level2_spellslotsparty_level3_spellslotsparty_level4_spellslotsparty_level5_spellslotsparty_level6_spellslotsparty_level7_spellslotsparty_level8_spellslotsparty_level9_spellslotsparty_total_acparty_total_precombat_hpparty_total_postcombat_hpparty_total_hpratioparty_total_prof_bonusparty_total_strengthparty_total_dexterityparty_total_constitutionparty_total_intelligenceparty_total_wisdomparty_total_charismaplayer_monster_ratiomonster_player_ratioBlood HunterRangerBardRogueWarlockWizardDruidMonkPaladinFighterBarbarianClericSorcererweighted_monster_levelweighted_spell_slots
3361665920216-a2e86d9c-3d86-4d43-9002-ff88d26a3bec1.665920e+09['327601077009165698'][{'hp_ratio': (0, 33), 'class': [('Cleric', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 21, 'stats': {'prof_bonus': 4, 'strength': 14, 'dexterity': 16, 'constitution': 12, 'intelligence': 12, 'wisdom': 16, 'charisma': 14}}][{'monster_id': 'cafdccad-f66b-434a-9ec4-bf01e9afec9c', 'monster_code': 'AN2', 'monster_name': 'Ankheg', 'level': 2.0}, {'monster_id': '5bc1d98a-4c46-473b-b4f9-242524dcd830', 'monster_code': 'AN1', 'monster_name': 'Ankheg', 'level': 2.0}, {'monster_id': 'a0e25490-f987-4e02-952d-04f345cc3f94', 'monster_code': 'TR1', 'monster_name': 'Troll', 'level': 5.0}]1{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Cleric', 5)]['Cleric'][(0, 33)][21][4][14][16][12][12][16][14]['Ankheg', 'Ankheg', 'Troll']39.05432000000213300.041416121216140.3333333.0000000000001018.00139
3371663470207-c4c65d03-8035-47f0-acae-f4adbf6ad2401.663470e+09['655070966261933416'][{'hp_ratio': (0, 35), 'class': [('Rogue', 4)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 2, 'strength': 9, 'dexterity': 20, 'constitution': 17, 'intelligence': 10, 'wisdom': 15, 'charisma': 13}}][{'monster_id': 'e6677640-5540-4141-98d4-da66f4572bbc', 'monster_code': 'WE1', 'monster_name': 'Wererat', 'level': 2.0}, {'monster_id': 'ad8491ef-09a7-4b65-8500-fb5eb02c0d14', 'monster_code': 'WE2', 'monster_name': 'Wererat', 'level': 2.0}, {'monster_id': '77653f25-3768-4d75-844a-7449c040e18f', 'monster_code': 'WE4', 'monster_name': 'Wererat', 'level': 2.0}, {'monster_id': '750fd006-36b1-4585-8dac-7d46295259b3', 'monster_code': 'WE3', 'monster_name': 'Wererat', 'level': 2.0}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Rogue', 4)]['Rogue'][(0, 35)][16][2][9][20][17][10][15][13]['Wererat', 'Wererat', 'Wererat', 'Wererat']48.04000000000163500.02920171015130.2500004.0000100000000016.000
3381663901818-3ac3c159-33b2-411b-a312-a7173a9046ce1.663902e+09['753313111965843135'][{'hp_ratio': (0, 44), 'class': [('Wizard', 6), ('Cleric', 2)], 'slots': {'1': 4, '2': 3, '3': 1, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 3, 'strength': 9, 'dexterity': 16, 'constitution': 12, 'intelligence': 20, 'wisdom': 13, 'charisma': 14}}][{'monster_id': '2488ed0e-5115-4b03-8e5d-de9607e01f0d', 'monster_code': 'HH1', 'monster_name': 'Hook Horror', 'level': 3.0}]1{'1': 4, '2': 3, '3': 1, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Wizard', 6), ('Cleric', 2)]['Wizard', 'Cleric'][(0, 44)][16][3][9][16][12][20][13][14]['Hook Horror']13.08431000000164400.03916122013141.0000001.000000100000103.00107
3391668588955-670519a4-752b-46d2-af92-9d3587c715291.668589e+09['812784442415776715'][{'hp_ratio': (0, 103), 'class': [('Paladin', 8), ('Warlock', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 15, 'dexterity': 13, 'constitution': 14, 'intelligence': 9, 'wisdom': 11, 'charisma': 20}}][{'monster_id': 'b27efcb3-5329-44e9-a62b-4018707e5275', 'monster_code': 'KLAC1', 'monster_name': 'King Lucius Artorius Castus', 'level': 30.0}]1{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Paladin', 8), ('Warlock', 5)]['Paladin', 'Warlock'][(0, 103)][19][5][15][13][14][9][11][20]['King Lucius Artorius Castus']130.0134320000001910300.05151314911201.0000001.0000010001000030.00139
3401663039706-4deda67f-fe96-43f0-937d-5c29b89a62c21.663040e+09['194822567355007767'][{'hp_ratio': (0, 32), 'class': [('Wizard', 6)], 'slots': {'1': 3, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 14, 'stats': {'prof_bonus': 3, 'strength': 9, 'dexterity': 17, 'constitution': 12, 'intelligence': 20, 'wisdom': 16, 'charisma': 18}}][{'monster_id': '838e4630-bc5e-420a-ad04-9d8182f96dbc', 'monster_code': 'Thunderbeast', 'monster_name': 'Thunderbeast Skeleton', 'level': 3.0}]1{'1': 3, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Wizard', 6)]['Wizard'][(0, 32)][14][3][9][17][12][20][16][18]['Thunderbeast Skeleton']13.06333000000143200.03917122016181.0000001.000000100000003.00165
3411659514852-40c80ad0-0aa2-48bd-9863-db8dc8ba21d71.659515e+09['249183932578417691'][{'hp_ratio': (0, 121), 'class': [('Ranger', 13)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 5, 'strength': 16, 'dexterity': 20, 'constitution': 16, 'intelligence': 12, 'wisdom': 17, 'charisma': 12}}][{'monster_id': '7e3950c4-78fd-487f-b420-e3444a4c4c81', 'monster_code': 'ATD1', 'monster_name': 'Adult Topaz Dragon', 'level': 13.0}]1{'1': 4, '2': 3, '3': 3, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Ranger', 13)]['Ranger'][(0, 121)][18][5][16][20][16][12][17][12]['Adult Topaz Dragon']113.0134331000001812100.051620161217121.0000001.0010000000000013.00215
3421660382749-5819fa97-4e90-4348-af20-434e148cf9b51.660383e+09['127142831326649106'][{'hp_ratio': (0, 143), 'class': [('Druid', 20)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}, 'ac': 29, 'stats': {'prof_bonus': 6, 'strength': 9, 'dexterity': 13, 'constitution': 14, 'intelligence': 11, 'wisdom': 20, 'charisma': 17}}][{'monster_id': 'ed01fcb2-cd0b-4fd7-8035-e47154716c5f', 'monster_code': 'CB3', 'monster_name': 'Clockwork Behir', 'level': 11.0}, {'monster_id': 'ca5f36ba-fdf4-4418-90b2-b50c4033abf9', 'monster_code': 'CB6', 'monster_name': 'Clockwork Behir', 'level': 11.0}, {'monster_id': '15489fdc-a358-40de-896a-7c017d9ed78b', 'monster_code': 'CB2', 'monster_name': 'Clockwork Behir', 'level': 11.0}, {'monster_id': '896f1b46-4be7-4128-9507-3d987a6d0c66', 'monster_code': 'CB4', 'monster_name': 'Clockwork Behir', 'level': 11.0}, {'monster_id': '08dd2c60-47db-424f-9cae-cba9e8bfa180', 'monster_code': 'CB5', 'monster_name': 'Clockwork Behir', 'level': 11.0}, {'monster_id': '85ea061f-6d0c-4445-b12d-4d6ec42688b7', 'monster_code': 'CB1', 'monster_name': 'Clockwork Behir', 'level': 11.0}, {'monster_id': '715f41e0-48d5-42f4-96d0-263c946181f0', 'monster_code': 'Wildshaped Talia', 'monster_name': 'Air Elemental', 'level': 5.0}, {'monster_id': 'ee740fd9-8edc-41fb-8d50-74cda74450b1', 'monster_code': 'AE1', 'monster_name': 'Air Elemental', 'level': 5.0}]1{'1': 4, '2': 3, '3': 2, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}[('Druid', 20)]['Druid'][(0, 143)][29][6][9][13][14][11][20][17]['Clockwork Behir', 'Clockwork Behir', 'Clockwork Behir', 'Clockwork Behir', 'Clockwork Behir', 'Clockwork Behir', 'Air Elemental', 'Air Elemental']876.0204323321112914300.06913141120170.1250008.00000001000000190.00919
3431660953025-7a93df7f-d06b-4f43-85fd-fbfb2966b7ed1.660953e+09['927715511664261819'][{'hp_ratio': (0, 47), 'class': [('Wizard', 9)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 14, 'stats': {'prof_bonus': 4, 'strength': 9, 'dexterity': 16, 'constitution': 13, 'intelligence': 18, 'wisdom': 13, 'charisma': 11}}][{'monster_id': '086a1464-e77a-472e-8103-ec82cf8023c0', 'monster_code': 'Stolverri', 'monster_name': 'Stolverri', 'level': 17.0}]1{'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}[('Wizard', 9)]['Wizard'][(0, 47)][14][4][9][16][13][18][13][11]['Stolverri']117.09433310000144700.04916131813111.0000001.0000001000000017.00367
3441664738914-8027b0fb-3e47-4b63-ab89-3a9bddf0f43e1.664739e+09['655070966261933416'][{'hp_ratio': (0, 23), 'class': [('Warlock', 2), ('Sorcerer', 1)], 'slots': {'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 14, 'stats': {'prof_bonus': 2, 'strength': 9, 'dexterity': 17, 'constitution': 14, 'intelligence': 10, 'wisdom': 12, 'charisma': 20}}][{'monster_id': 'bc8a898c-87b0-43bb-8cfd-0a0c2d01b6be', 'monster_code': 'TR1', 'monster_name': 'Troglodyte', 'level': 0.25}, {'monster_id': '53590604-9ae1-4680-9ebe-c93dbf9d7be6', 'monster_code': 'TR2', 'monster_name': 'Troglodyte', 'level': 0.25}]1{'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Warlock', 2), ('Sorcerer', 1)]['Warlock', 'Sorcerer'][(0, 23)][14][2][9][17][14][10][12][20]['Troglodyte', 'Troglodyte']20.53400000000142300.02917141012200.5000002.000001000000010.7524
3451668686129-1c743b81-2d8b-4266-a63e-8181bdaf407b1.668686e+09['335741543258656549'][{'hp_ratio': (1, 224), 'class': [('Fighter', 20)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 26, 'stats': {'prof_bonus': 6, 'strength': 13, 'dexterity': 22, 'constitution': 20, 'intelligence': 10, 'wisdom': 20, 'charisma': 10}}][{'monster_id': '2db111bd-3c10-47f6-b37c-e8cd7b24cb38', 'monster_code': 'KI2', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': 'cd33594b-abb9-49b5-afec-6719f07eced9', 'monster_code': 'KI3', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': 'd46f027b-7251-494f-bbb2-eb76dff53470', 'monster_code': 'KI1', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': 'a09c982d-fff7-407f-b08f-78342335c316', 'monster_code': 'KI2', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': 'd7212bb3-94b9-4ab8-b0c7-881ecb62f444', 'monster_code': 'KI4', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': '17949b3d-2b69-40bc-b146-257b9133e402', 'monster_code': 'KI1', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': '4db0d0d8-0dd8-4b3a-8a29-ef62daae57a0', 'monster_code': 'KI3', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': 'b78b4554-1a8f-45a1-bf7b-a723cd806199', 'monster_code': 'KI5', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': '128af718-40b0-4dd3-8be9-d33c4cad2f82', 'monster_code': 'KI6', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': '42de7904-657d-4510-81cd-057f60243350', 'monster_code': 'KI8', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': '15ac2216-cd5d-463c-b6a6-66cff0f2bcca', 'monster_code': 'KI11', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': 'a4752c34-1509-4b74-9e51-f333b204a05d', 'monster_code': 'KI10', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': '3495a098-0620-43c6-a1da-e244bb5df31d', 'monster_code': 'KI9', 'monster_name': 'Kirov', 'level': 14.0}, {'monster_id': 'a5596278-4efd-44bf-b6e5-6a64f907cf28', 'monster_code': 'KI7', 'monster_name': 'Kirov', 'level': 14.0}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Fighter', 20)]['Fighter'][(1, 224)][26][6][13][22][20][10][20][10]['Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov', 'Kirov']14196.0200000000002622410.061322201020100.07142914.00000000001000588.000